Capítulo 9 Classificação

Davi Moreira, Mônica Rocabado

Como apresentado por Grimmer e Stewart (2013), os métodos de classificação automatizada, podem ser divididos em dois grupos:

Categorias conhecidas e
Categorias desconhecidas.

Quadro Grimmer e Stweart (2013)

9.1 Categorias conhecidas

Ancorados na teoria, na experiência ou especialidade em determinado assunto pode-se classificar um acervo de documentos em categorias já conhecidas. As aplicações são inúmeras, mas entre elas descatam-se duas:

Métodos de dicionário; e
Métodos de aprendizagem supervisionada (supervised learning methods).

9.1.1 Método de dicionário

O método de dicionário se refere, de forma ampla, ao uso tanto de padrões de texto em palavras chaves, quanto do uso de buscas booleanas complexas e expressões regulares para contar com que frequência certos conceitos e/ou códigos e/ou padrões ocorrem no documento analisado (Moreira, Pires, and Medeiros 2022). Esta é uma abordagem dedutiva, pois o dicionário define a priori quais códigos serão mensurados e, com isto, não é afetado pelos dados a serem analisados.

O uso combinado do método com técnicas de Processamento de Linguagem Natural (PLN) torna possível análises mais aprofundadas do corpus, como o expressões de sentimento atribuídas a atores específicos, ou ações e afetos de um ator em relação a outro (Atteveldt et al. 2017).

9.1.1.1 Análise de sentimento

Como destacam Izumi e Moreira (2018), a análise de sentimentos pode ser realizada com o uso de um dicionário de termos. O pacote quanteda possui funções que auxiliam nessa tarefa. Através das funções tokens_lookup() ou dfm_looup() pode-se contar os valores do dicionário. O quanteda contém o Lexicoder Sentiment Dictionary criado por Young e Soroka (2012). Ver também: quanteda.sentiment para textos em inglês.

Para a língua portuguesa, o Grupo de Processamento da Linguagem Natural da Pontifícia Universidade Católica do Rio Grande do Sul (PUC-RS) possui o OpLexicon Vieira and Souza (2011). Esse dicionário apresenta 31.719 termos dos quais 14.254 carregam sentimentos negativos, 8.469 sentimentos positivos e 8.996 sentimentos neutros.

Usando a base de dados do jornal The Guardian presente no pacote quanteda, iremos aplicar o método para analisar os sentimentos nos artigos que mencionam a União Europeia. Para uso da base de dados é necessário o pacote quanteda.corpora, que pode ser baixada através de devtools::install_github("quanteda/quanteda.corpora").

library(quanteda)
library(quanteda.corpora)
library(stringi)
library(stringr)
library(lubridate)
library(tidyverse)
library(here)

corp_news <- download("data_corpus_guardian")

Com o corpus, incluímos as variáveis referentes ao ano, mês e semana, tendo como base a variável date. Em seguida, filtramos o corpus para obter artigos de 2016 até o período mais recente da base.

# Criando novas variáveis ao nível do documento
docvars(corp_news, 'year') <- year(docvars(corp_news, 'date'))
docvars(corp_news, 'month') <- month(docvars(corp_news, 'date'))
docvars(corp_news, 'week') <- week(docvars(corp_news, 'date'))

# Filtro do variável ano
corp_news <- corpus_subset(corp_news, 'year' >= 2016)

Para análise, devemos processar a base. Assim, vamos transformá-la em tokens, remover as pontuações e selecionar os documentos que mencionam a Únião Europeia.

# Transformando em tokens
toks_news <- tokens(corp_news, remove_punct = TRUE)

# Filtrar os documentos que mencionam a UE
eu <- c('EU', 'europ*', 'european union')
toks_eu <- tokens_keep(toks_news, pattern = phrase(eu), window = 10)

Antes de aplicar o dicionário de sentimento (Lexicoder Sentiment Dictionary), podemos verificar que ele possui quatro códigos, cada qual possuindo termos e expressões referentes a cada um dos códigos.

lengths(data_dictionary_LSD2015)

##     negative     positive neg_positive neg_negative 
##         2858         1709         1721         2860

Como nosso intuito é analisar somente os termos positivos e negativos relacionados aos documentos que mencionam a UE após e em 2016, vamos selecionar somente os dois primeiros códigos.

data_dictionary_LSD2015_pos_neg <- data_dictionary_LSD2015[1:2]

Aplicamos então o dicionário a nossa base previamente tratada com a função tokens_lookup() e a transformamos em um dfm:

#Aplicando o dicionário a nossa base
toks_eu_lsd <- tokens_lookup(toks_eu, dictionary = data_dictionary_LSD2015_pos_neg)

#Transformando em DFM
dfmat_news_lsd <- dfm(toks_eu_lsd)

Para melhor visualização dos dados agrupamos a nossa dfm por semana:

# Agrupando a DFM
dfmat_eu_lsd <- dfm_group(dfmat_news_lsd, groups = week)

#Visualizando os dados
matplot(dfmat_eu_lsd, type = 'l', xaxt = 'n', lty = 1, ylab = 'Frequency')
grid()
axis(1, seq_len(ndoc(dfmat_eu_lsd)), ymd("2016-01-01") + 
       weeks(seq_len(ndoc(dfmat_eu_lsd)) - 1))
legend('topleft', col = 1:2, legend = c('Negative', 'Positive'), lty = 1, bg = 'white')

Podemos também visualizar as menções positiva vs as negativas. Para isso, temos que realizar uma contagem dos tokens (total de features).

n_eu <- ntoken(dfmat_eu_lsd)

plot((dfmat_eu_lsd[,2] - dfmat_eu_lsd[,1]) / n_eu,
     type = 'l', ylab = 'Sentiment', xlab = '', xaxt = 'n')
axis(1, seq_len(ndoc(dfmat_eu_lsd)), ymd("2016-01-01") + 
       weeks(seq_len(ndoc(dfmat_eu_lsd)) - 1))
grid()
abline(h = 0, lty = 2)

9.1.1.1.1 Atividade prática

Repita a análise acima para os tokens c('immig*', 'migra*').

9.1.1.2 Dicionário de tópico - Policy Agenda

CAP

Para além de códigos positivos e negativos, existem outros projetos de dicionários com códigos mais complexos, este é caso do LexiCoder Policy Agenda, dicionário que captura os principais tópicos presentes no projeto Comparative Agenda Policy³.

Para realizar os exemplos abaixo é necessário baixar o dicionário dos códigos referentes ao Policy Agenda em formato .lcd, que pode ser realizado por esse link. Como até o momento os dicionários são somente em inglês ou holandês, iremos analisar a base de debates na Assembéia Geral da ONU, UN General Debate, de 2017 presente no pacote quanteda.corpora.

UNGDspeeches <- quanteda.corpora::data_corpus_ungd2017
dict <- dictionary(file = here("data/policy_agendas_english.lcd"))

Antes de aplicar o dicionário, vamos processar a base, retirando informações no texto que não auxiliam nossa análise como números, pontuações e stop_words. Também agrupamos nossa dfm de acordo com o país que realizou o discurso.

UNGD_token <-  tokens(UNGDspeeches, split_hyphens = TRUE, remove_numbers = TRUE, 
                 remove_punct = TRUE, remove_symbols = TRUE, remove_url = TRUE,
                 include_docvars = TRUE)

UNGD_dfm <- dfm(UNGD_token, tolower = TRUE, remove = stopwords("en"))

UNGD_dfm_p <- dfm_group(UNGD_dfm, groups = country)

Com a base pronta, aplicamos o dicionário através do dfm_lookup e obtemos uma DFM que contém os principais temas dos discursos dos países em 2017.

UNGD_dfm_code <- dfm_lookup(UNGD_dfm_p, dictionary = dict)

doc_id	macroeconomics	civil_rights	healthcare	agriculture	forestry	labour	immigration	education	environment	energy	fisheries	transportation	crime	social_welfare	housing	finance	defence	sstc	foreign_trade	intl_affairs	government_ops	land-water-management	culture	prov_local	constitutional_natl_unity	aboriginal	religion
Afghanistan	0	8	0	0	0	4	2	1	1	1	0	2	1	2	0	1	3	0	0	8	1	1	0	0	0	0	0
Albania	0	8	0	0	0	1	0	0	0	2	0	0	1	0	0	0	2	0	0	7	0	1	0	0	0	0	0
Algeria	0	6	0	0	0	0	0	0	0	0	0	1	4	2	0	0	1	1	0	0	0	0	0	0	1	0	0
Andorra	1	9	0	0	0	0	0	0	11	5	0	0	3	0	0	0	0	0	0	10	0	1	0	0	1	0	0
Angola	0	9	0	0	0	1	0	0	2	0	0	0	0	1	0	0	2	0	2	6	0	0	0	0	0	0	0
Antigua & Barbuda	8	1	1	0	0	7	0	2	0	1	0	0	6	3	1	1	0	0	0	5	0	0	0	0	0	0	0
Argentina	3	7	0	1	0	2	0	0	6	0	0	0	6	4	0	3	0	0	2	7	0	0	0	0	1	0	1
Armenia	0	8	0	0	0	0	0	2	0	0	0	0	2	1	0	0	4	0	0	1	0	0	0	0	0	0	0
Australia	2	7	0	0	1	2	0	0	2	0	0	0	0	1	0	0	2	0	0	8	0	0	0	0	0	0	0
Austria	0	3	0	0	0	0	1	0	0	0	0	1	2	1	0	0	1	0	0	4	0	0	0	0	0	0	0
Azerbaijan	5	8	4	0	0	1	1	0	0	2	0	3	2	2	0	1	3	0	1	3	2	0	0	0	0	0	1
Bahamas	5	12	4	0	0	1	0	0	5	3	0	0	3	2	0	4	0	1	0	12	0	1	0	0	0	0	0
Bahrain	0	4	1	0	0	1	0	0	0	2	0	0	2	2	0	0	3	0	0	5	0	0	0	0	1	0	7
Bangladesh	2	3	2	2	0	3	2	2	2	0	0	0	4	4	0	0	4	1	1	2	1	1	0	0	0	0	0
Barbados	0	9	7	0	0	2	0	0	7	0	1	1	1	0	0	2	0	0	1	7	0	2	0	0	0	0	1
Belarus	0	1	0	0	0	0	0	0	0	0	0	2	0	1	0	1	7	0	0	8	0	1	0	0	0	0	0
Belgium	0	2	0	0	0	0	1	1	1	0	0	0	5	1	0	0	1	0	0	4	0	0	1	0	0	0	0
Belize	13	1	2	0	0	3	1	0	3	1	0	1	8	0	0	5	0	0	1	12	0	0	0	0	1	0	0
Benin	0	1	1	0	0	1	0	0	3	0	0	2	0	3	0	1	0	0	0	1	0	2	0	0	0	0	0
Bhutan	0	1	1	0	1	0	0	0	1	1	0	1	1	13	0	0	1	0	1	11	0	0	0	0	0	0	0
Bolivia	0	5	1	0	0	0	3	0	0	2	0	0	10	1	0	2	6	0	3	5	0	1	1	0	1	0	1
Bosnia & Herzegovina	1	3	0	0	0	1	2	0	1	0	0	0	2	1	0	0	2	0	0	7	0	0	0	0	0	0	0
Botswana	2	9	1	0	0	4	0	0	1	0	0	0	2	5	1	0	3	0	0	3	0	0	0	0	0	0	0
Brazil	3	10	0	0	1	0	3	0	0	2	0	0	5	1	0	3	2	0	0	4	0	0	0	0	1	0	0
Brunei	0	2	2	0	0	1	1	0	3	1	0	0	1	1	0	0	3	0	0	5	0	0	0	0	0	0	0
Bulgaria	0	16	0	0	0	0	3	0	1	0	0	0	0	1	0	0	6	0	0	10	0	1	0	0	0	0	0
Burkina Faso	0	0	0	0	0	0	0	0	0	0	0	0	2	1	0	0	1	0	1	3	0	0	0	0	1	0	0
Burundi	0	7	0	0	0	0	0	0	0	0	0	0	2	0	0	0	2	0	0	2	0	0	0	0	0	0	0
Cambodia	1	10	0	0	0	0	1	1	0	0	0	0	0	3	0	0	1	0	1	5	0	0	0	0	0	0	0
Cameroon	0	0	0	0	1	0	0	0	0	0	0	0	0	6	0	0	3	0	0	2	0	0	0	0	0	0	0
Canada	4	25	2	0	0	0	0	0	4	0	0	0	0	3	3	0	0	0	1	2	0	1	0	0	0	6	0
Cape Verde	0	4	0	0	0	1	1	1	1	1	0	3	5	1	0	0	1	0	0	6	1	1	0	0	1	0	0
Central African Republic	1	2	0	1	0	0	0	0	1	1	0	2	8	3	0	0	2	0	0	2	0	1	0	0	0	0	0
Chad	0	2	0	0	0	1	0	0	0	0	0	1	0	3	0	0	1	0	1	2	0	0	0	0	0	0	1
Chile	1	5	0	0	0	7	0	1	4	3	0	0	3	0	0	0	2	0	0	4	0	0	0	0	1	0	0
China	0	4	0	0	0	0	0	1	0	0	0	4	1	1	0	0	3	0	1	10	0	1	0	0	0	0	0
Colombia	0	3	5	0	0	0	1	1	0	0	0	0	4	2	1	0	6	0	0	1	0	0	0	0	0	0	0
Comoros	0	7	1	0	0	0	0	0	1	6	0	3	1	0	0	0	1	0	0	5	0	2	0	0	0	0	3
Congo - Brazzaville	0	1	3	0	0	0	0	0	2	0	0	2	1	1	0	0	1	0	0	4	0	2	0	0	0	0	0
Congo - Kinshasa	0	1	0	0	2	0	0	0	0	0	0	0	5	0	0	0	1	0	0	2	0	0	0	0	0	0	0
Costa Rica	3	30	3	1	0	10	1	0	11	3	0	2	6	4	0	0	5	0	1	12	0	1	2	0	0	0	1
Côte d’Ivoire	1	1	0	0	0	0	0	0	4	1	0	0	0	0	0	0	2	0	0	8	0	0	0	0	0	0	0
Croatia	0	7	0	0	0	1	3	0	2	0	0	0	3	2	0	0	4	0	0	14	0	0	0	0	0	0	0
Cuba	1	9	2	0	0	1	1	0	1	0	0	1	3	3	1	1	6	0	3	2	0	2	0	0	0	0	0
Cyprus	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	2	0	0	9	0	1	0	0	1	0	0
Czechia	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	2	0	0	0	1
Denmark	1	14	1	0	0	1	1	0	0	0	0	0	0	2	0	0	3	0	0	6	0	1	0	0	0	0	0
Djibouti	2	1	1	0	0	1	0	0	1	0	0	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	1
Dominica	0	0	2	0	0	1	0	0	0	1	0	3	0	1	0	0	8	0	0	5	0	1	0	0	0	0	0
Dominican Republic	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Ecuador	7	3	3	0	0	0	1	0	3	0	0	0	0	2	2	0	3	0	0	2	0	0	1	0	0	0	1
Egypt	0	2	0	0	0	0	2	0	0	0	0	0	2	2	0	0	2	0	0	2	0	2	0	0	0	0	2
El Salvador	2	3	2	0	0	4	0	0	1	0	0	0	3	3	1	1	0	0	0	3	0	0	0	0	0	0	0
Equatorial Guinea	0	4	1	0	0	0	0	0	2	0	0	0	1	1	0	0	2	0	0	3	0	1	0	0	0	0	0
Eritrea	1	1	2	0	0	0	0	0	2	0	0	1	0	2	0	0	2	1	0	1	0	0	0	0	0	0	0
Estonia	0	8	1	0	0	0	1	0	0	0	0	1	1	2	0	0	2	2	0	11	0	1	0	0	0	0	0
Ethiopia	0	0	0	0	0	1	0	0	2	0	0	0	0	7	0	0	3	0	0	11	0	0	0	0	0	0	0
European Union	1	0	0	0	0	0	2	0	0	0	0	0	2	0	0	0	2	1	0	19	0	0	0	0	0	0	1
Fiji	0	0	1	1	0	0	0	1	0	2	0	0	0	0	0	0	0	0	0	14	0	1	0	0	0	0	0
Finland	0	7	5	0	0	0	0	0	0	2	0	1	2	0	0	0	4	0	0	9	0	0	0	0	0	0	0
France	1	5	4	0	0	0	3	2	1	1	0	7	4	1	0	0	6	1	0	6	0	1	0	0	0	0	0
Gabon	2	0	1	0	0	0	0	0	2	0	0	1	3	0	0	1	0	0	2	2	0	1	0	0	0	0	0
Gambia	3	4	1	0	0	2	0	0	1	0	0	1	8	1	0	0	0	0	0	5	0	1	0	0	0	0	1
Georgia	3	3	2	0	0	1	1	0	1	0	0	6	2	1	0	2	1	1	0	11	0	1	1	0	2	0	0
Germany	1	2	0	0	0	0	1	1	0	0	0	2	3	1	0	0	7	0	0	7	0	2	0	0	0	0	0
Ghana	0	0	1	0	0	0	0	2	0	1	0	1	2	2	0	0	0	1	0	3	0	0	0	0	0	0	0
Greece	0	10	0	0	0	0	1	0	0	1	0	0	2	0	0	0	5	0	0	3	0	0	1	0	0	0	0
Grenada	3	2	9	0	0	0	0	0	1	0	0	2	0	0	0	3	2	0	1	7	0	0	1	0	0	0	0
Guatemala	1	6	1	0	0	1	0	0	2	0	0	0	9	1	0	0	2	0	0	2	0	1	0	0	1	0	0
Guinea	1	0	1	0	0	0	1	0	0	3	0	0	1	4	0	1	1	1	0	1	0	0	0	0	0	0	0
Guinea-Bissau	1	5	6	1	0	0	0	0	2	0	0	1	0	8	0	0	0	0	2	1	0	0	0	0	0	0	0
Guyana	0	4	0	0	4	0	2	0	6	0	0	0	10	2	0	0	1	0	1	4	0	1	0	0	0	0	0
Haiti	4	1	5	0	0	0	0	0	3	3	0	1	2	0	1	0	1	0	0	4	1	0	0	0	1	0	0
Honduras	5	6	2	0	0	5	0	0	1	0	0	1	5	6	0	1	0	0	1	2	1	0	0	0	0	0	0
Hungary	0	13	1	0	0	0	3	0	0	0	0	0	0	0	0	0	3	1	0	4	0	0	0	0	0	0	0
Iceland	0	16	3	0	0	0	0	0	1	7	0	0	0	3	0	0	1	0	0	1	0	0	0	0	0	0	0
India	3	5	1	0	1	2	0	1	0	0	0	0	0	6	0	1	1	0	1	2	0	0	0	0	0	0	0
Indonesia	0	1	1	0	0	1	0	0	3	0	0	0	0	3	0	0	1	0	0	17	0	0	0	0	0	0	0
Iran	0	11	0	0	0	0	0	0	2	1	0	5	2	2	0	1	7	0	2	10	0	0	0	0	0	0	1
Iraq	0	4	2	0	0	1	2	1	2	2	0	0	9	0	1	0	5	0	0	2	0	1	0	0	5	0	0
Ireland	0	2	0	0	0	0	0	0	3	2	0	0	0	4	0	0	3	0	0	14	0	3	0	0	0	0	0
Israel	0	0	6	1	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	5	0	0	0	0	0	0	3
Italy	0	5	1	0	0	1	0	0	1	1	0	3	2	1	0	1	2	1	0	9	0	1	0	0	0	0	1
Jamaica	4	1	2	0	0	5	0	1	5	0	0	1	0	0	0	5	3	0	4	10	0	0	0	0	0	0	0
Japan	0	0	1	0	0	1	0	1	0	5	0	0	0	1	0	0	12	0	0	1	0	0	0	0	0	0	0
Jordan	4	0	1	0	1	1	1	0	0	3	0	0	1	0	1	1	2	0	1	6	0	0	0	0	0	0	3
Kazakhstan	1	2	0	0	0	0	0	0	1	6	0	4	0	1	0	0	9	2	0	13	0	0	0	0	0	0	0
Kenya	1	1	5	0	0	0	3	0	4	0	0	0	1	1	0	0	3	0	0	8	0	0	0	0	0	0	0
Kiribati	5	3	3	0	0	3	0	0	3	0	1	0	0	1	0	1	0	0	0	21	0	1	0	0	0	0	4
Kuwait	0	2	0	0	0	0	2	0	1	0	0	0	0	1	0	0	1	0	0	2	0	1	0	0	0	0	1
Kyrgyzstan	1	7	1	0	0	0	2	0	2	4	0	1	1	1	0	0	2	0	1	5	1	3	0	0	0	0	1
Laos	0	2	0	0	0	1	0	1	3	0	0	2	0	2	0	0	0	0	1	3	0	0	0	0	0	0	0
Latvia	0	3	0	0	0	0	0	0	2	0	0	0	2	2	0	0	1	0	0	15	0	1	0	0	0	0	0
Lebanon	0	7	1	0	0	3	8	0	1	0	0	0	4	2	0	2	17	0	0	1	0	0	0	0	0	0	1
Lesotho	3	4	6	0	0	3	2	0	2	1	0	1	2	5	0	0	6	0	1	6	0	1	0	0	0	0	2
Liberia	0	2	4	0	0	0	3	1	1	1	0	2	1	2	0	1	1	0	0	5	0	2	0	0	0	0	0
Libya	0	8	0	0	0	0	1	0	0	0	0	1	0	0	0	0	3	0	1	0	0	1	0	0	0	0	0
Liechtenstein	0	5	0	0	0	0	1	1	0	0	0	0	15	0	0	0	4	0	0	2	0	1	1	0	0	0	0
Lithuania	0	1	0	0	0	0	1	1	0	1	0	0	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0
Luxembourg	1	10	5	0	0	1	2	0	1	0	0	0	4	0	0	0	4	0	0	3	0	4	0	0	1	0	0
Macedonia	0	6	0	0	0	1	1	0	2	0	0	0	0	0	0	0	5	0	0	4	0	0	0	0	0	0	0
Madagascar	5	1	11	1	0	2	0	1	2	2	0	0	0	5	0	2	0	0	0	11	0	0	0	0	0	0	0
Malawi	0	3	1	3	0	1	0	1	0	0	0	0	1	1	0	0	1	0	0	9	0	1	0	0	0	0	0
Malaysia	0	2	0	0	0	2	0	0	2	0	0	0	1	0	0	0	5	0	0	3	0	0	0	0	0	0	0
Maldives	2	11	5	0	0	3	1	0	2	7	2	0	1	1	3	0	5	0	0	8	0	1	0	0	1	0	0
Mali	2	2	10	0	0	0	1	0	3	0	0	0	5	1	0	0	0	1	0	5	0	2	0	0	0	0	0
Malta	0	14	0	0	0	1	0	1	1	0	0	0	3	4	0	0	1	0	0	3	0	2	0	0	1	0	0
Marshall Islands	0	2	3	0	0	0	0	1	1	1	1	2	1	1	0	1	3	0	0	8	0	1	0	0	0	0	0
Mauritania	0	7	0	0	0	0	1	0	0	2	0	1	3	0	0	1	0	0	0	0	0	0	0	0	0	0	0
Mauritius	0	1	0	0	0	5	0	1	0	0	0	0	7	0	0	1	0	0	0	1	0	0	0	0	0	0	0
Mexico	2	5	1	1	0	1	0	0	0	0	0	0	0	1	0	2	1	0	0	3	0	0	0	0	0	0	0
Micronesia (Federated States of)	0	4	1	0	0	0	0	0	4	1	1	0	0	1	0	0	0	0	0	8	0	0	0	0	0	0	0
Moldova	0	3	0	0	0	0	0	0	1	1	0	0	0	2	0	2	1	0	0	2	0	0	0	0	1	0	0
Monaco	0	5	2	0	0	0	0	0	2	0	0	1	1	3	0	0	1	0	0	4	0	1	0	0	0	0	0
Mongolia	0	4	1	0	1	2	3	0	0	7	0	5	2	2	0	1	4	0	2	12	0	0	0	0	0	0	0
Montenegro	0	15	0	0	0	0	1	0	0	0	0	0	0	3	0	0	5	0	0	4	0	0	0	0	0	0	0
Morocco	1	1	0	0	0	1	1	0	0	3	0	1	2	1	0	1	2	0	0	5	0	0	0	0	0	0	1
Mozambique	1	3	0	0	0	1	0	0	2	1	0	0	0	1	0	0	2	0	2	4	0	1	0	0	1	0	0
Myanmar (Burma)	0	4	0	0	0	0	1	0	0	0	0	2	5	1	0	0	1	0	0	1	0	0	0	0	0	0	2
Namibia	0	4	0	1	0	3	0	0	0	0	0	0	1	7	0	0	4	0	1	3	0	2	1	0	0	0	0
Nauru	0	1	3	2	0	2	0	1	0	1	0	1	0	0	1	0	1	0	0	4	0	0	0	0	0	0	1
Nepal	0	15	0	0	0	0	0	2	1	1	0	1	4	2	0	0	1	0	0	8	0	0	0	2	5	0	0
Netherlands	0	1	0	0	0	0	0	1	1	0	0	0	3	3	0	0	2	0	0	3	0	0	0	0	0	0	0
New Zealand	0	0	0	0	0	2	1	0	0	4	0	0	0	0	0	1	6	0	0	2	0	3	0	1	0	0	0
Nicaragua	0	5	0	0	0	0	0	0	0	0	0	0	11	7	0	0	2	0	1	2	0	1	0	0	0	0	0
Niger	0	3	0	0	0	0	0	1	2	0	0	0	3	1	0	0	2	0	1	0	0	0	0	0	1	0	1
Nigeria	0	3	0	0	0	1	0	0	0	0	0	0	1	0	0	0	2	0	0	1	0	0	0	0	0	0	0
North Korea	0	0	0	0	0	0	0	0	0	0	0	1	13	0	0	0	18	1	1	2	0	1	0	0	0	0	0
Norway	2	2	1	0	0	0	0	2	6	0	0	2	0	4	0	0	4	0	2	13	0	0	0	0	0	0	0
Oman	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	3	0	2	6	0	3	1	0	0	0	0
Pakistan	1	5	1	0	0	0	5	0	0	1	0	2	4	2	0	0	15	0	1	9	1	1	0	0	0	0	0
Palau	0	1	0	0	0	0	0	0	3	0	1	1	0	1	0	0	0	0	0	10	0	1	0	0	1	0	0
Palestinian Territories	0	6	1	0	0	0	1	0	0	1	0	1	7	0	0	0	1	0	0	3	0	1	0	0	0	0	9
Panama	4	0	3	0	0	1	0	0	0	0	0	4	10	3	1	0	2	0	0	0	0	1	0	0	0	0	3
Papua New Guinea	1	3	4	0	0	0	1	1	1	0	1	3	0	0	0	1	2	0	0	9	0	0	0	0	0	0	0
Paraguay	1	7	0	0	1	0	0	1	0	1	0	2	4	9	2	1	0	0	0	9	0	2	0	0	1	0	2
Peru	0	5	0	0	0	0	0	0	1	0	0	0	0	1	0	0	2	0	0	3	0	0	0	0	0	0	0
Philippines	1	20	2	1	0	0	0	1	3	0	0	0	5	4	0	0	9	0	0	5	0	0	0	1	0	0	1
Poland	0	6	1	0	0	1	1	1	3	1	0	0	0	2	0	0	4	0	0	4	0	1	0	0	0	0	1
Portugal	0	11	1	0	0	1	0	0	3	0	0	0	0	0	0	0	1	0	0	10	0	1	0	0	0	0	0
Qatar	0	7	1	0	0	0	1	0	0	0	0	0	2	0	0	0	4	0	0	1	0	0	0	0	0	0	1
Romania	0	4	0	0	0	0	0	0	0	0	0	0	2	1	0	0	2	0	0	4	0	0	0	0	0	0	0
Russia	0	5	2	0	0	0	1	0	0	1	0	1	1	0	0	0	10	1	1	7	0	1	0	0	0	1	1
Rwanda	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0
Samoa	0	4	0	0	0	0	0	0	4	0	0	5	4	1	0	0	1	0	0	10	0	1	0	0	0	0	0
San Marino	0	17	1	0	0	0	0	0	0	2	0	1	4	5	0	0	1	0	0	7	0	0	0	0	0	0	0
São Tomé & Príncipe	0	4	0	0	0	0	0	0	2	0	0	0	0	1	0	0	1	0	1	8	0	4	0	0	0	0	0
Saudi Arabia	0	1	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Senegal	1	1	0	0	0	0	0	1	1	0	0	0	0	1	0	0	3	0	0	3	0	0	0	0	0	0	3
Serbia	3	2	0	0	0	2	1	0	0	0	0	0	1	1	0	2	1	0	0	8	0	0	0	0	0	0	0
Seychelles	2	1	0	0	0	0	1	0	4	0	0	1	0	0	0	0	0	0	0	4	0	0	0	0	0	0	0
Sierra Leone	0	5	6	0	0	2	1	0	2	1	0	0	2	0	2	0	5	0	0	8	0	0	0	0	0	0	0
Singapore	3	1	2	0	0	0	0	0	2	0	0	0	0	1	0	0	2	1	1	13	0	1	0	0	1	0	0
Slovakia	0	0	0	0	0	0	1	0	4	0	0	0	0	2	0	0	1	0	0	1	0	1	0	0	0	0	0
Slovenia	0	11	1	0	0	0	1	0	3	0	0	1	7	0	0	0	3	0	0	11	0	0	0	0	0	0	0
Solomon Islands	1	11	1	0	0	2	0	0	2	2	1	0	2	1	0	1	2	0	0	10	0	0	1	0	0	0	0
Somalia	2	7	1	3	0	0	2	0	1	0	0	2	2	2	0	0	1	0	1	2	0	3	0	0	0	0	0
South Africa	3	3	3	0	0	0	0	0	1	2	0	0	0	2	1	0	4	0	0	9	0	1	0	0	0	0	0
South Korea	0	2	0	0	0	1	1	0	0	1	0	0	0	0	0	0	18	0	0	3	0	2	4	0	1	0	0
South Sudan	0	0	0	2	0	0	1	0	3	0	0	1	1	0	0	0	3	0	0	0	0	1	0	0	0	0	0
Spain	0	9	0	0	0	0	0	0	0	0	0	1	1	1	0	1	6	0	0	3	0	3	0	0	2	1	0
Sri Lanka	1	12	0	0	0	0	0	0	0	0	0	0	0	3	0	0	3	0	0	1	0	0	0	0	1	0	0
St. Kitts & Nevis	1	1	3	1	0	2	0	1	1	2	0	1	1	1	0	0	4	0	1	7	0	0	0	0	0	0	0
St. Lucia	0	1	1	0	0	1	0	0	2	0	0	0	1	0	1	0	0	0	0	6	0	1	0	1	0	0	0
St. Vincent & Grenadines	0	3	3	0	0	2	1	0	0	3	0	0	0	0	0	2	4	0	0	9	0	1	0	0	0	0	1
Sudan	4	1	0	0	0	0	0	0	1	0	0	2	22	1	0	0	2	0	0	1	0	0	0	0	0	0	0
Suriname	4	6	3	0	2	2	0	0	3	0	0	1	1	0	1	0	1	0	3	10	0	2	2	0	1	1	1
Swaziland	0	1	11	1	0	3	0	3	2	0	0	0	0	2	0	2	0	0	0	5	0	1	0	0	0	0	0
Sweden	4	6	0	0	0	2	0	1	0	0	0	0	0	2	0	0	2	0	0	5	0	0	0	0	0	0	0
Switzerland	0	2	0	0	0	0	1	0	0	0	0	1	0	1	0	0	2	2	0	5	0	1	0	0	0	0	0
Syria	0	1	4	0	0	0	1	0	0	1	0	0	1	0	0	0	20	1	0	3	0	0	1	0	0	0	0
Tajikistan	1	1	0	0	0	0	1	0	0	6	0	5	1	1	0	0	1	0	0	1	0	0	0	0	0	0	2
Tanzania	2	0	0	0	0	3	0	0	0	0	0	0	0	3	0	0	4	0	0	3	0	1	0	0	0	0	0
Thailand	0	3	14	0	0	0	0	2	2	0	0	0	1	2	0	0	0	0	0	2	0	0	0	0	1	0	0
Timor-Leste	0	2	4	0	0	0	1	3	0	0	0	0	3	2	0	0	3	0	1	2	0	1	0	0	0	0	0
Togo	2	5	8	0	0	0	0	2	1	1	0	0	1	1	0	0	1	0	0	1	0	0	0	0	3	0	0
Tonga	0	3	2	0	0	0	0	0	5	7	0	0	0	0	0	0	0	0	0	2	0	1	0	0	0	0	1
Trinidad & Tobago	1	8	1	0	0	1	0	0	5	0	0	0	6	1	0	0	1	0	3	8	0	0	0	0	1	0	0
Tunisia	1	8	0	0	0	1	1	0	1	0	0	1	4	1	0	0	2	0	0	0	0	0	0	0	1	0	0
Turkey	1	6	2	0	0	1	1	0	1	0	0	0	2	4	0	0	2	0	0	3	0	1	0	0	0	0	5
Turkmenistan	0	3	0	0	0	0	1	0	2	7	0	4	0	0	0	0	0	0	0	14	0	1	5	0	0	0	0
Tuvalu	0	18	3	0	0	0	1	1	5	2	0	1	1	0	0	0	0	0	0	17	0	1	0	0	0	0	1
Uganda	0	0	1	3	0	0	0	0	0	0	0	1	1	0	0	0	2	1	0	4	0	2	0	0	0	0	1
Ukraine	0	9	3	0	0	0	5	1	0	2	0	0	15	1	0	0	7	0	0	4	0	0	1	0	0	0	0
United Arab Emirates	0	1	2	0	0	0	0	0	1	1	0	0	2	0	0	0	0	0	0	2	0	1	0	0	0	0	2
United Kingdom	2	3	1	0	0	1	1	0	0	1	0	0	3	0	0	0	3	3	0	15	0	0	0	0	0	0	1
United States	2	6	4	0	0	2	2	3	0	1	0	2	5	3	0	1	6	2	0	5	3	1	0	0	3	0	5
Uruguay	0	4	3	0	0	0	0	0	0	1	0	0	5	2	0	0	12	0	0	9	0	0	0	0	0	0	0
Uzbekistan	1	8	0	0	0	3	1	0	0	1	0	2	0	0	0	0	0	0	1	3	1	0	1	0	0	0	6
Vanuatu	1	8	5	0	0	0	0	1	0	1	0	1	1	4	0	0	3	0	1	3	0	0	0	0	0	0	1
Vatican City	0	13	0	0	0	0	2	0	6	0	0	0	3	6	1	0	20	0	0	4	0	1	0	0	0	0	23
Venezuela	2	7	2	0	0	1	1	0	0	2	0	0	7	3	1	0	7	0	1	2	1	4	0	0	1	0	0
Vietnam	0	2	1	0	0	2	0	0	0	0	0	1	0	2	0	0	3	0	1	6	0	0	0	0	0	0	0
Yemen	0	1	0	0	0	0	0	1	0	0	0	1	1	1	0	0	4	0	0	0	0	0	0	0	0	0	3
Zambia	1	5	2	0	0	0	0	0	4	0	0	1	7	3	0	0	0	0	0	1	0	0	0	0	0	0	0
Zimbabwe	0	1	0	0	0	0	0	1	1	0	0	0	1	3	0	0	2	1	0	8	0	0	0	0	0	0	1

Outra forma de visualizar a dfm é convertendo-a em data.frame. Assim, facilitamos o uso do pacote ggplot2 para produção de gráficos e visualizações.

UNGD_df <- convert(UNGD_dfm_code, "data.frame") %>%
  cbind(docvars(UNGD_dfm_code)) %>%
  gather(macroeconomics:religion, key = "Topic", value = "Share") %>%
  select(country, Topic, Share, continent) %>% 
  group_by(country) %>%
  mutate(Share = Share / sum(Share)) %>%
  mutate(Topic = as_factor(Topic))

Com a base codificada em formato data.frame, podemos visualizar a proporção dos principais tópicos debatidos na ONU ao longo do ano pelo Brasil.

UNGD_df %>%
filter(country == 'Brazil',
       Share != 0) %>% 
  mutate(Topic = fct_reorder(Topic, Share)) %>% 
  ggplot(aes(Topic, Share)) +
  geom_bar(stat = "identity") +
  labs(title = "Distribuição dos tópicos da PA no discurso do Brasil na ONU em 2017",
       subtitle = 'UN General Debate corpus',
       x = '', y ='') +
  coord_flip()

9.1.1.3 Construindo seu próprio dicionário

A função dictionary() do pacote quanteda permite a construção de um dicionário de códigos pelo usuário. No exemplo abaixo, iremos utilizar a base dos votos proferidos pelos deputados federais na sessão de aprovação do impeachment da Presidenta Dilma Rousseff em Abril de 2016.

Para baixar os dados do Impeachment utilize o pacote txt4cs

#devtools::install_github("davi-moreira/txt4cs-pkg", force = T) download do pacote caso ainda não o tenha

impeachment_dilma <- txt4cs::impeachment_dilma

Utilizando a função dictionary(), vamos criar um dicionário com expressões específicas.

dict_impeach <- dictionary(list(golpe = "golp", 
                                deus = c("deus", "jesus", "cristo", 
                                         "cristao"),
                          familia = c("familia", "filh(o|a)", 
                                      "net(o|a)", "avo", "pai", "mae"),
                          patria = c("patria", "nacao")))

A seguir, processamos a base para obter nossa dfm.

impeachment_dfm_party <- impeachment_dilma %>%
  mutate(text = stri_trans_general(text, "Latin-ASCII")) %>%
#  filter(partido == "PSL") %>% select(nomeOrador, text)
 corpus(docid_field = "doc_id", text_field = "text") %>%
#  print(impeachment_dfm_party, max_ndoc = 3, max_nchar = 50)
  tokens(remove_punct = TRUE, remove_numbers = T) %>%
  tokens_tolower() %>%
  tokens_remove(stopwords(source = "stopwords-iso", language = "pt"), min_nchar = 2) %>%
  # tokens_wordstem(language = "pt") %>%
  dfm() %>%
  dfm_remove(pattern = c('sr', 'senhor',
                         'presidente', 'presidencia', 
                         'esclarecimentos', 'total', 'deputado*',
                         'beto', 'mansur', 'palma*', 
                         'eduardo', 'cunha', 'vota*',
                         'process*', 'discurs*', 'lido*')) %>%
  dfm_group(groups = partido)

Em seguida, aplicamos o nosso dicionário criado.

#Aplicando o dicionário

dict_dtm <- dfm_lookup(impeachment_dfm_party, dictionary = dict_impeach, 
                       valuetype = "regex", nomatch = "_unmatched")

Podemos transformar a nossa DFM em um data.frame para usar a infraestrutura de processamento do projeto tidyverse. Dessa forma, com pacote ggplot2 visualizamos quais partidos mais mencionaram os códigos do dicionário.

impeach_df <- convert(dict_dtm, "data.frame") %>%
  bind_cols(docvars(dict_dtm)) %>% 
  pivot_longer(golpe:patria, names_to = "topic", values_to = "n") %>%
  select(partido, topic, n) %>% 
  group_by(partido, topic) %>% 
  summarise(count = sum(n)) %>% 
  mutate(perc = count/sum(count)) %>% 
  mutate(topic = factor(topic, levels = c("golpe", 
                                              "patria",
                                              "familia",
                                              "deus"))) %>%
  group_by(partido) %>% 
  arrange(topic, perc)

cores <- c("#000000",
           "#787878",
            "#C0C0C0",
           "#DCDCDC")


impeach_df %>% 
  mutate(partido = factor(partido, levels = unique(impeach_df$partido))) %>%
  ggplot(aes(x = partido, y = perc, fill = topic)) +
    geom_bar(stat="identity", width = 0.7) +
    scale_fill_manual (values = cores) +
    coord_flip() + theme_bw() + 
    labs(title = 'Termos mencionados nos disursos por partido', fill = 'Tópico') +
    theme(axis.title.x = element_blank(), 
        axis.title.y = element_blank(),
        #        legend.key = element_rect(color = "gray", fill = "black"),
        legend.title = element_blank()
        )

9.1.2 Ressalvas

Segundo Grimmer e Stewart (2013), o objetivo principal do método é automatizar a codificação manual dos documentos a partir de categorias ou métricas previamente definidas. Logo, se o método estiver perfomando bem, irá replicar a codificação manual. Por isso, o método de validação recomendado é a comparação do resultado da codificação da máquina com aqueles obtidos através de codificação manual do seguinte modo:

Realiza-se o ajuste inicial do modelo aplicado ao training set;
Um segundo conjunto de documentos manualmente codificados seriam usados para avaliar o desempenho do modelo utilizado no passo 1;
Definido o modelo final, este é aplicado ao test set para completar a classificação do acervo.

A validação cruzada pode ser utilizada para replicar esse procedimento ideal.

9.1.3 Métodos de aprendizagem supervisionada

Métodos de aprendizado supervisionado replicam a familiar tarefa de codificação manual, porém com redução de custos e ganho de escala. Sua implementação pressupõe a classificação manual de uma amostra do acervo em um conjunto predeterminado de categorias. Essa amostra classificada, conhecida como conjunto de treinamento ou training set, é usada para treinar modelos estatísticos, cuja principal aplicação é a classificação do restante do acervo, o conjunto de teste ou test set. Ao final da classificação, procedimentos de validação devem ser adotados para se averiguar a performance do modelo utilizado.

Métodos de aprendizado supervisionado requerem que os pesquisadores desenvolvam regras de codificação manual para as categorias de interesse. Tal necessidade força os analistas a desenvolverem definições coerentes de conceitos para aplicações particulares, o que leva à clareza sobre a classificação pretendida. Outra vantagem dos métodos de aprendizado supervisionado para classificação é a facilidade de validação e verificação da performance do modelo utilizado.

Como apontam Grimmer e Stewart (2013), todos os métodos de aprendizagem supervisionada pressupõem três etapas básicas após os procedimentos de pré-processamento:

Construir um conjunto de treinamento (training set)

Esquema de codificação: Para a construção de um conjunto de treinamento, deve ser criado um esquema/plano de codificação manual que supere dificuldades relacionadas a ambiguidades na linguagem, a atenção limitada dos codificadores e o entendimento sobre conceitos presentes no acervo. Com um livro de códigos elaborado, devem-se realizar exercícios de testes para que sejam identificadas ambiguidades no esquema de codificação ou nas categorias negligenciadas. Esse procedimento leva, subsequentemente, a uma revisão do livro de códigos, que então precisa ser aplicado a um novo conjunto de documentos para assegurar que as ambiguidades tenham sido suficientemente resolvidas. Logo, somente após os codificadores aplicarem o esquema de codificação aos documentos do acervo sem perceber ambiguidades, o esquema estará pronto para ser aplicado ao restante do conjunto de dados^[Para mais detalhes, ver Krippendorff (2004), Neuendorf (2002) e a documentação disponível no pacote ReadMe de Hopkins e King (2010).
Seleção do conjunto de treinamento: Idealmente, os documentos presentes no conjunto de treinamento devem ser representativos do acervo. Logo, para um bom desempenho do modelo, é aconselhável que o conjunto de treinamento seja construído a partir de uma amostra aleatória da coletânea à qual pertencem. Isto posto, resta saber qual a quantidade ideal de documentos para o conjunto de treinamento. Hopkins e King (2010) indicam quinhentos como regra geral, sendo cem documentos já suficientes para alguns casos. No entanto, o número ideal dependerá da aplicação específica de interesse, pois, conforme o número de categorias aumenta, mais documentos são necessários em cada categoria do conjunto de treinamento para uma boa performance do modelo.

Aplicar o método de aprendizado supervisionado

Os métodos de aprendizagem supervisionada são diversos, mas compartilham de uma estrutura comum. Cada modelo de aprendizagem supervisionada assume que existe uma função $f$ , não observada, que associa o vocabulário de palavras dos documentos às categorias preestabelecidas. Assim, com base no conjunto de treinamento, o algoritmo “interpreta” essa associação e a replica aos demais documentos.

Validar, validar, validar

Como já vimos, a validação é extremamente importante quando utilizamos métodos de aprendizagem computacional. Para modelos de classificação supervisionada, esse requisito não é diferente. Veremos adiante com mais detalhes como empreender essa tarefa através de modelos de classificação supervisionada.

9.1.3.1 Naive Bayes

Similar ao método de dicionário, a classificação pelo método Naive Bayes também requer uma categorização prévia do texto em $k$ categorias. Logo, para seu funcionamento, exige que a base seja dividida entre training set e test set. O conjunto de treinamento (training set) é usado para aprender sobre a distribuição de palavras dos documentos de cada categoria $k$ . Essa distribuição é usada para classificar cada um dos documentos no conjunto restante do acervo (test set). Esse é um dos mais simples e poderosos métodos de classificação individual por não demandar tempo ou memória excessiva da máquina.

Teorema de Bayes

Com base no teorema de Bayes⁴, o modelo infere a probabilidade de que o documento $i$ pertença à categoria $k$ , dado o perfil de palavras $P_i$ . Sendo $C_k$ uma categoria qualquer, pelo teorema de Bayes temos $P(C_k|P_i) \propto P(C_k)P(P_i|C_k)$ . Logo, é necessário estimar $P(C_k)$ e $P(P_i|C_k)$ . Após o modelo ser treinado, essas probabilidades são passadas ao test set que irá calcular a probabilidade de cada termo pertencer a cada categoria. O modelo opera a classiﬁcação ao inferir a probabilidade de que o documento $i$ pertença à categoria $k$ , dado o perﬁl de palavras $P_i$ .

Sendo o conjunto de treinamento uma boa representação do acervo, temos que o estimador de máxima verossimilhança de $P(C_k)$ é dado pela proporção de documentos do conjunto de treinamento em cada categoria $k$ . Por sua vez, a estimação de $P(P_i|C_k)$ é mais complexa e necessita do pressuposto ingênuo (naive assumption) de que, dada a categoria de um documento, as palavras são geradas de forma independente. Mesmo com esse pressuposto equivocado, o modelo ainda é capaz de capturar informações úteis para classiﬁcação dos documentos⁵ de uma rica literatura que inclui outros modelos como: Random Forests [Breiman (2001), Support Vector Machines (Venables and Ripley 2002) e Redes Neurais (Bishop, Bishop, and Bishop 1996).

Para realizar a estimação com o Naive Bayes é necessário⁶:

Definir uma base de treinamento ( training set ) e outra de teste ( test set ) baseadas no corpus.
Gerar uma DFM baseada nesses dados.
Treinar o algorítmo que será alimentado pela base de treinamento.
Checar a perfomance dos resultados (accuracy).
Comparar os dados com uma predição aleatória.

Como exemplo prático, vamos aplicar o Naive Bayes a um acervo de 2.000 análises críticas de filmes que indicam se este apresenta conotação positiva ou negativa. Neste exemplo, usamos 1.500 comentários como conjunto de treinamento (training set). Em seguida, estimamos as categorias para as revisões restantes (test set).

library(quanteda)
library(quanteda.textmodels)
library(caret)
library(e1071)

Primeiro, obtemos nossa base com críticas dos filmes presente no pacote quanteda.textmodels. A variável sentiment indica se um filme foi classificado como positivo ou negativo.

corp_movies <- quanteda.textmodels::data_corpus_moviereviews

Text	Types	Tokens	Sentences	sentiment	id1	id2
cv000_29416.txt	354	841	9	neg	cv000	29416
cv001_19502.txt	156	278	1	neg	cv001	19502
cv002_17424.txt	276	553	3	neg	cv002	17424
cv003_12683.txt	313	555	2	neg	cv003	12683
cv004_12641.txt	380	841	2	neg	cv004	12641

Para podermos trabalhar com a base é necessário processá-la. Sendo assim, criamos uma variável que corresponde ao id numérico dos filmes classificados. Em seguida transformamos a nossa base em tokens e em uma dfm.

docvars(corp_movies, "id_numeric") <- 1:ndoc(corp_movies)

toks_movies <- tokens(corp_movies, remove_punct = TRUE, remove_number = TRUE) %>% 
               tokens_remove(pattern = stopwords("en")) %>% 
               tokens_wordstem()

dfmt_movie <- dfm(toks_movies)

Para seleção de nossa base de treinamento e de teste, iremos primeiro gerar uma amostra aleatória de 1500 números que podem variar de 1 a 2000. Com este número referente a base de treinamento, filtramos nossa dfm para que o id dos filmes corresponda ao id númerico aleatório, obtendo assim nossa dfm de treinamento. Para dfm de teste o processo é similar, mas filtramos os 500 registros que não correspondem ao id treinamento.

# id aleatório
set.seed(300)
id_train <- sample(1:2000, 1500, replace = FALSE)

# training set
dfmat_training <- dfm_subset(dfmt_movie, id_numeric %in% id_train)

# test set 
dfmat_test <- dfm_subset(dfmt_movie, !id_numeric %in% id_train)

Com a base pronta, treinamos o modelo Naive Bayes através da função textmodel_nb, colocando o resultado no objeto tmod_nb.

tmod_nb <- textmodel_nb(dfmat_training, docvars(dfmat_training, "sentiment"))

Como vimos na explicação da teoria, o modelo Naive Bayes consegue apenas levar em consideração as features que ocorrem tanto na base treinamento, quanto na base de teste. Para isso, é necessário tornar as features identicas em ambas dfms usando a função dfm_match(). No caso, obtemos uma base em que as features da base de teste só permanecem na análise caso também ocorram na base de treinamento.

dfmat_matched <- dfm_match(dfmat_test, features = featnames(dfmat_training))

Agora, vamos criar dois objetos para nos auxiliar na validação do modelo: i) a classe prevista, somente com as 500 classificações da base de teste; e ii) a classe atual, utilizada para o treinamento.

actual_class <- dfmat_matched$sentiment
predicted_class <- predict(tmod_nb, newdata = dfmat_matched)

	x
cv000_29416.txt	neg
cv013_10494.txt	neg
cv032_23718.txt	pos
cv033_25680.txt	neg
cv036_18385.txt	neg
cv038_9781.txt	neg

9.1.3.2 Validar a saída do modelo

Se o método de aprendizagem supervisionada aplicado tiver bom desempenho, ele será capaz de se assemelhar à classificação manual nas tarefas de codificação de documentos em categorias. Esse objetivo claro implica um padrão preciso para sua avaliação, ou seja: a comparação da saída da codificação automatizada com a saída da codificação manual.

Logo, o procedimento de validação ideal se divide em três⁷:

o ajuste inicial do modelo realizado no conjunto de treinamento;
depois que um modelo final é escolhido, um segundo conjunto de documentos codificados manualmente - o conjunto de validação - deve ser usado para avaliar o desempenho do modelo;
em seguida, que o modelo final seja aplicado ao restante do acervo (test set) para completar a classificação.

tab_class <- table(actual_class, predicted_class)

	neg	pos
neg	213	45
pos	37	205

Na tabela cruzada, vemos que o número de falsos positivos e falsos negativos é semelhante. O classificador cometeu erros em ambas as direções, mas não parece superestimar ou subestimar uma classe.

Podemos usar a função confusionMatrix() do pacote caret para avaliar o desempenho da classificação.

confusion <- confusionMatrix(tab_class, mode = "everything")
confusion

## Confusion Matrix and Statistics
## 
##             predicted_class
## actual_class neg pos
##          neg 213  45
##          pos  37 205
##                                           
##                Accuracy : 0.836           
##                  95% CI : (0.8006, 0.8674)
##     No Information Rate : 0.5             
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.672           
##                                           
##  Mcnemar's Test P-Value : 0.4395          
##                                           
##             Sensitivity : 0.8520          
##             Specificity : 0.8200          
##          Pos Pred Value : 0.8256          
##          Neg Pred Value : 0.8471          
##               Precision : 0.8256          
##                  Recall : 0.8520          
##                      F1 : 0.8386          
##              Prevalence : 0.5000          
##          Detection Rate : 0.4260          
##    Detection Prevalence : 0.5160          
##       Balanced Accuracy : 0.8360          
##                                           
##        'Positive' Class : neg             
##

Precision, Recall e F1 são medidas frequentemente usadas para avaliar o desempenho da classificação.

Precision resulta de $\frac{TP}{TP + FP}$ , onde $TP$ é o número de verdadeiros positivos e $FP$ os falsos positivos.
Recall divide os falsos positivos pela soma de positivos verdadeiros e falsos negativos $\frac{TP}{(TP + FN)}$ .
A pontuação F1 é uma média harmônica de Precision e Recall $2\times \frac{(Precision \times Recall)}{Precision + Recall}$ .

Podemos visualizar o resultado transformando a matriz em data.frame.

confusion.data <- as.data.frame(confusion[["table"]])

ggplot(confusion.data,
       aes(x = predicted_class, y = actual_class, fill = Freq)) +
  xlab("Predicted class") +
  ylab("Actual class") +
  geom_tile() + theme_bw() + coord_equal() +
  scale_fill_distiller(palette = "Blues", direction = 1)

9.2 Categorias desconhecidas

Vimos que os métodos de aprendizagem supervisionada auxiliam na tarefa de classificação do acervo a partir de um conhecimento prévio sobre suas categorias. Contudo, não é difícil encontrar situações nas quais o conjunto de categorias seja desconhecido. Pode, por exemplo, ser do interesse de um pesquisador identificar quais tópicos são enfatizados pelos deputados e deputadas federais nos discursos proferidos ao longo de diversas legislaturas (Moreira 2020). Uma vez que a atividade do representante político se debruça sobre inúmeras esferas da sociedade e da vida, predeterminar categorias temáticas de fala dos deputados e deputadas federais pode limitar o conhecimento a ser obtido sobre o acervo. Para enfrentar esse desafio, a seguir apresentamos métodos de aprendizado não supervisionado (unsupervised learning methods).

9.2.1 Métodos de aprendizagem não supervisionada

A classe de métodos de aprendizado não supervisionado revela características subjacentes ao texto sem a necessidade de indicação de categorias de interesse. Ao invés de exigir a determinação prévia das categorias, os métodos de aprendizado não supervisionado usam premissas e propriedades de modelagem dos textos para estimar um conjunto de categorias e, simultaneamente, atribuir a elas os documentos (ou partes de seu conteúdo).

Ao informar para o algoritmo o número $k$ de categorias nas quais os documentos devem ser alocados, há a oportunidade de se descobrir qual a composição de categorias que apresenta melhor aderência ao conteúdo em análise. Dada a incerteza do pesquisador sobre a performance do modelo em relação ao acervo, procedimentos de validação são essenciais.

9.2.1.1 Topic Models: a modelagem de tópicos

Os modelos de tópicos possuem duas principais características:

definem estatisticamente um tópico como função densidade de probabilidade sobre palavras. Para um tópico $k$ $(k = 1, . . . , K)$ , essa função de probabilidade é representada com um vetor $M \times 1$ , $\theta_k$ , em que $\theta_{mk}$ descreve a probabilidade de o $k$ −ésimo tópico usar a $m$ −ésima palavra. Logo, para estimar um tópico, os modelos usam a ocorrência de palavras entre documentos e pressupõem, em sua grande maioria, o uso da DFM obtida através do pré-processamento dos dados.
os modelos de tópicos compartilham uma estrutura hierárquica básica. Como apresenta a Figura 9.1. Para cada modelo temos o elemento sobre o qual os tópicos estão distribuídos. Em outras palavras, o elemento que terá uma distribuição de tópicos que somada resulta em um ou 100%. Em seguida, na parte inferior da hierarquia, palavras ou documentos que são atribuídos aos tópicos. O Expressed Agenda Model (Grimmer 2010), por exemplo, pressupõe que cada documento seja classificado em apenas um tópico.

Figure 9.1: Modelos de Tópicos - (Izumi e Moreira, 2018)

Como apontam Grimmer e Stewart (2013), todos os métodos de aprendizagem não supervisionada pressupõem duas etapas básicas após os procedimentos de pré-processamento dos dados:

1. Definindo o número $k$ de categorias

Determinar o número de categorias é uma das tarefas mais complexas no aprendizado não supervisionado. Ao mensurar quão bem modelos se ajustam aos dados, medidas de ajuste estatístico tornam-se inúteis diante da brusca redução de informação que o uso de métodos não supervisionados para análise de conteúdo pressupõem após o pré-processamento dos dados. Os textos pré-processados representam uma simplificação substancial dos documentos, sendo o objetivo do uso de métodos não supervisionados a revelação de informações latentes e substantivamente relevantes. Logo, em vez de ajuste estatístico, a seleção de modelos deve ser tratada como um problema de mensuração substantiva. Em linha com Grimmer e Stewart (2013), a abordagem mixed-method fornecida por Quinn et al. (2010) é adequada. Nela, os modelos candidatos são ajustados variando-se o número $k$ de categorias para, em seguida, ser realizada uma avaliação manual e qualitativa de seleção do modelo final com base na qualidade das categorias obtidas pelos diferentes modelos.

2. Validação

Se, de um lado, o uso da aprendizagem não supervisionada reduz os custos de análise manual do acervo antes da aplicação do modelo, de outro, a carga de trabalho para a validação de seus resultados é relevante. É a validação extensiva das categorias estimadas e dos documentos classificados que permite a realização de inferências concretas sobre o acervo^[Para conhecer um exemplo aplicado referente ao uso do Expressed Agenda Model, veja Moreira (2020). Para conhecer uma abordagem mais recente sobre validação, veja Ying et. al (2022).

9.2.1.1.1 LDA: Latent Dirichlet Allocation

O Latent Dirichlet Allocation (LDA) (D. Blei and Jordan 2003) é um método popular para modelagem de tópicos. Ele trata cada documento como uma mistura de tópicos e cada tópico como uma mistura de palavras. Isso permite que os documentos “se sobreponham” uns aos outros em termos de conteúdo, em vez de serem separados em grupos distintos como seria numa análise de cluster padrão. Em outra palavras:

cada documento consiste em uma distribuição de tópicos. Ou seja, um documento pode estar em mais de um tópico.
cada tópico consiste em uma distribuição de termos (features). Os termos (features) podem estar presentes em mais de um tópico.

Dessa forma, o LDA nos auxilia a encontrar as palavras associadas com cada tópico, enquanto também determina os tópicos e o quanto eles descrevem cada documento. Alguns termos (features) possuem maior probabilidade de aparecer em um tópico e outros menos, o mesmo vale para os documentos.

Vale ressaltar que o resultado não apresenta automaticamente o nome ou rótulo de cada tópico estimado. É necessária uma análise qualitativa para compreender o resultado dos termos em cada tópico e a diferença entre os tópicos, dado que os termos podem se repetir entre os tópicos, mudando a probabilidade em cada um (Ying, Montgomery, and Stewart 2022).

Figure 9.2: Probabilistic Topic Models - LDA (Blei, 2012)

Para aplicação do LDA, faremos uso do pacote topicmodels, que implementa o modelo desenvolvido por Blei et al D. M. Blei (2013). Usaremos uma DFM contida no pacote que possui 2.246 documentos (linhas) e 10.473 (colunas).

library(topicmodels)
library(tidytext)
library(ggplot2)
library(dplyr)
library(tidyr)

data("AssociatedPress")

Vamos utilizar $k$ categorias igual a 2 para aplicar nosso modelo de LDA na base AssociatedPress

ap_lda <- LDA(AssociatedPress, k = 2, control = list(seed = 123))

Podemos visualizar assim as palavras mais associadas a cada tópico:

terms(ap_lda, 10)

##       Topic 1   Topic 2     
##  [1,] "percent" "i"         
##  [2,] "million" "president" 
##  [3,] "new"     "government"
##  [4,] "year"    "people"    
##  [5,] "billion" "soviet"    
##  [6,] "last"    "new"       
##  [7,] "two"     "bush"      
##  [8,] "company" "two"       
##  [9,] "people"  "years"     
## [10,] "market"  "states"

Assim como quais os tópicos mais provavéis para cada documento:

head(topics(ap_lda), 20)

##  [1] 2 2 1 2 2 2 1 2 1 2 1 2 2 2 1 1 2 2 1 1

9.2.1.1.1.1 Word-topic probabilities

O pacote tidytext permite facilmente extrair as probabilidades $\beta$ de cada palavra por tópico, ou seja a probabilidade de que o termos seja gerado a partir de cada tópico.

ap_topics <- tidy(ap_lda, matrix = "beta")

topic	term	beta
1	aaron	0.0000000
2	aaron	0.0000390
1	abandon	0.0000265
2	abandon	0.0000399
1	abandoned	0.0001391
2	abandoned	0.0000588
1	abandoning	0.0000000
2	abandoning	0.0000234
1	abbott	0.0000021
2	abbott	0.0000297
1	abboud	0.0000447
2	abboud	0.0000000
1	abc	0.0003646
2	abc	0.0000026
1	abcs	0.0000924
2	abcs	0.0000095
1	abctvs	0.0000075
2	abctvs	0.0000221
1	abdomen	0.0000359
2	abdomen	0.0000100
1	abducted	0.0000000
2	abducted	0.0000351
1	abduction	0.0000282
2	abduction	0.0000154
1	abductors	0.0000000
2	abductors	0.0000234
1	abdul	0.0000218
2	abdul	0.0000082
1	abide	0.0000000
2	abide	0.0000234
1	abilities	0.0000250
2	abilities	0.0000098
1	ability	0.0001038
2	ability	0.0001418
1	ablaze	0.0000508
2	ablaze	0.0000152
1	able	0.0004080
2	able	0.0003152
1	abm	0.0000000
2	abm	0.0000273
1	aboard	0.0004014
2	aboard	0.0000354
1	abolished	0.0000006
2	abolished	0.0000346
1	abortion	0.0000000
2	abortion	0.0002922
1	abortions	0.0000000
2	abortions	0.0000779
1	abraham	0.0000000
2	abraham	0.0000390
1	abrams	0.0000000
2	abrams	0.0000662
1	abroad	0.0000388
2	abroad	0.0001911
1	abrupt	0.0000180
2	abrupt	0.0000108
1	absence	0.0000473
2	absence	0.0000293
1	absent	0.0000277
2	absent	0.0000157
1	absolute	0.0000420
2	absolute	0.0000174
1	absolutely	0.0000503
2	absolutely	0.0001051
1	absorbed	0.0000351
2	absorbed	0.0000067
1	abu	0.0000162
2	abu	0.0000354
1	abuse	0.0000614
2	abuse	0.0001364
1	abused	0.0000000
2	abused	0.0000584
1	abuses	0.0000000
2	abuses	0.0001286
1	abyss	0.0000000
2	abyss	0.0000234
1	academic	0.0000165
2	academic	0.0000353
1	academics	0.0000000
2	academics	0.0000312
1	academy	0.0000002
2	academy	0.0002180
1	accelerated	0.0000272
2	accelerated	0.0000044
1	accept	0.0000289
2	accept	0.0002603
1	acceptable	0.0000008
2	acceptable	0.0000579
1	acceptance	0.0000350
2	acceptance	0.0000535
1	accepted	0.0000217
2	accepted	0.0002264
1	accepting	0.0000000
2	accepting	0.0000818
1	accepts	0.0000000
2	accepts	0.0000234
1	access	0.0001017
2	access	0.0002290
1	accessible	0.0000391
2	accessible	0.0000000
1	accident	0.0007256
2	accident	0.0000000
1	accidentally	0.0000614
2	accidentally	0.0000000
1	accidents	0.0002679
2	accidents	0.0000000
1	accommodate	0.0000093
2	accommodate	0.0000247
1	accompanied	0.0000714
2	accompanied	0.0000631
1	accompanying	0.0000447
2	accompanying	0.0000000
1	accomplish	0.0000000
2	accomplish	0.0000273
1	accomplished	0.0000163
2	accomplished	0.0000198
1	accomplishments	0.0000011
2	accomplishments	0.0000304
1	accord	0.0000393
2	accord	0.0001947
1	account	0.0002477
2	account	0.0001193
1	accountable	0.0000100
2	accountable	0.0000242
1	accountant	0.0000104
2	accountant	0.0000161
1	accounted	0.0000893
2	accounted	0.0000000
1	accounting	0.0001806
2	accounting	0.0000688
1	accounts	0.0002337
2	accounts	0.0001369
1	accurate	0.0000397
2	accurate	0.0000463
1	accusation	0.0000000
2	accusation	0.0000312
1	accusations	0.0000005
2	accusations	0.0000698
1	accuse	0.0000000
2	accuse	0.0000506
1	accused	0.0000006
2	accused	0.0008411
1	accuses	0.0000000
2	accuses	0.0000351
1	accusing	0.0000001
2	accusing	0.0000857
1	achieve	0.0000291
2	achieve	0.0000654
1	achieved	0.0000147
2	achieved	0.0000599
1	achieving	0.0000130
2	achieving	0.0000182
1	acid	0.0000378
2	acid	0.0000554
1	ackerman	0.0000000
2	ackerman	0.0000390
1	acknowledge	0.0000391
2	acknowledge	0.0000117
1	acknowledged	0.0001315
2	acknowledged	0.0002238
1	acknowledges	0.0000000
2	acknowledges	0.0000234
1	acknowledging	0.0000059
2	acknowledging	0.0000231
1	acquire	0.0001898
2	acquire	0.0000000
1	acquired	0.0002342
2	acquired	0.0000002
1	acquiring	0.0000693
2	acquiring	0.0000062
1	acquisition	0.0002764
2	acquisition	0.0000019
1	acquisitions	0.0000609
2	acquisitions	0.0000003
1	acquitted	0.0000000
2	acquitted	0.0000584
1	acre	0.0002455
2	acre	0.0000001
1	acreage	0.0000447
2	acreage	0.0000000
1	acres	0.0003014
2	acres	0.0000000
1	act	0.0000626
2	act	0.0004900
1	acted	0.0000154
2	acted	0.0001373
1	acting	0.0000751
2	acting	0.0001775
1	action	0.0002000
2	action	0.0007175
1	actions	0.0000533
2	actions	0.0002511
1	active	0.0002033
2	active	0.0000685
1	actively	0.0001475
2	actively	0.0000217
1	activist	0.0000000
2	activist	0.0001480
1	activists	0.0000056
2	activists	0.0002337
1	activities	0.0000427
2	activities	0.0002390
1	activity	0.0003302
2	activity	0.0000812
1	actor	0.0000006
2	actor	0.0002178
1	actors	0.0000000
2	actors	0.0000818
1	actress	0.0000000
2	actress	0.0001753
1	acts	0.0000000
2	acts	0.0001091
1	actual	0.0001172
2	actual	0.0000351
1	actually	0.0002471
2	actually	0.0000808
1	acustar	0.0000391
2	acustar	0.0000000
1	ad	0.0000430
2	ad	0.0000869
1	adam	0.0000000
2	adam	0.0000234
1	adams	0.0000358
2	adams	0.0000140
1	adapt	0.0000005
2	adapt	0.0000308
1	add	0.0001206
2	add	0.0000444
1	added	0.0005283
2	added	0.0006014
1	adding	0.0002017
2	adding	0.0001280
1	addition	0.0003778
2	addition	0.0002116
1	additional	0.0002014
2	additional	0.0002802
1	address	0.0000000
2	address	0.0003273
1	addressed	0.0000000
2	addressed	0.0000857
1	addressing	0.0000080
2	addressing	0.0000723
1	adds	0.0000429
2	adds	0.0000090
1	adequate	0.0000820
2	adequate	0.0000324
1	adequately	0.0000187
2	adequately	0.0000298
1	adhere	0.0000000
2	adhere	0.0000234
1	adjacent	0.0000309
2	adjacent	0.0000174
1	adjoining	0.0000213
2	adjoining	0.0000163
1	adjourned	0.0000000
2	adjourned	0.0000312
1	adjust	0.0000252
2	adjust	0.0000058
1	adjusted	0.0000670
2	adjusted	0.0000000
1	adjustment	0.0000668
2	adjustment	0.0000001
1	adjustments	0.0000333
2	adjustments	0.0000079
1	adkins	0.0000063
2	adkins	0.0000540
1	adm	0.0000309
2	adm	0.0000408
1	administered	0.0000192
2	administered	0.0000489
1	administration	0.0004310
2	administration	0.0013822
1	administrations	0.0000115
2	administrations	0.0001790
1	administrative	0.0000282
2	administrative	0.0001479
1	administrator	0.0000966
2	administrator	0.0000495
1	administrators	0.0000149
2	administrators	0.0000442
1	admission	0.0000002
2	admission	0.0000544
1	admit	0.0000040
2	admit	0.0000673
1	admits	0.0000038
2	admits	0.0000285
1	admitted	0.0000903
2	admitted	0.0001396
1	admitting	0.0000001
2	admitting	0.0000428
1	adn	0.0000000
2	adn	0.0000584
1	adolf	0.0000000
2	adolf	0.0000545
1	adopt	0.0000414
2	adopt	0.0000607
1	adopted	0.0000126
2	adopted	0.0002172
1	adoption	0.0000355
2	adoption	0.0000337
1	adoptive	0.0000212
2	adoptive	0.0000086
1	ads	0.0000307
2	ads	0.0001344
1	adult	0.0001091
2	adult	0.0000213
1	adults	0.0001261
2	adults	0.0000172
1	advance	0.0002865
2	advance	0.0000961
1	advanced	0.0002292
2	advanced	0.0000192
1	advancement	0.0000000
2	advancement	0.0000273
1	advances	0.0000954
2	advances	0.0000191
1	advancing	0.0001184
2	advancing	0.0000108
1	advantage	0.0000725
2	advantage	0.0000741
1	adventure	0.0000240
2	adventure	0.0000105
1	adverse	0.0000002
2	adverse	0.0000233
1	advertisements	0.0000370
2	advertisements	0.0000248
1	advertising	0.0001584
2	advertising	0.0000687
1	advice	0.0000343
2	advice	0.0001163
1	advise	0.0000000
2	advise	0.0000350
1	advised	0.0000272
2	advised	0.0000706
1	adviser	0.0000545
2	adviser	0.0001723
1	advisers	0.0000252
2	advisers	0.0001187
1	advises	0.0000152
2	advises	0.0000322
1	advisory	0.0000414
2	advisory	0.0000529
1	advocacy	0.0000183
2	advocacy	0.0000106
1	advocate	0.0000000
2	advocate	0.0000584
1	advocated	0.0000000
2	advocated	0.0000273
1	advocates	0.0000097
2	advocates	0.0000906
1	aeroflot	0.0000084
2	aeroflot	0.0000175
1	aeronautics	0.0000781
2	aeronautics	0.0000000
1	aerospace	0.0000781
2	aerospace	0.0000000
1	affair	0.0000091
2	affair	0.0001222
1	affairs	0.0000466
2	affairs	0.0003532
1	affect	0.0001214
2	affect	0.0000984
1	affected	0.0002564
2	affected	0.0000665
1	affecting	0.0000607
2	affecting	0.0000239
1	affects	0.0000502
2	affects	0.0000000
1	affidavit	0.0000000
2	affidavit	0.0000390
1	affiliated	0.0000715
2	affiliated	0.0000046
1	affluent	0.0000207
2	affluent	0.0000206
1	afford	0.0000917
2	afford	0.0000489
1	affordable	0.0000193
2	affordable	0.0000255
1	afghan	0.0000004
2	afghan	0.0000543
1	afghanistan	0.0000000
2	afghanistan	0.0001052
1	aflcio	0.0000000
2	aflcio	0.0000234
1	afraid	0.0000345
2	afraid	0.0000889
1	africa	0.0000017
2	africa	0.0007663
1	african	0.0000000
2	african	0.0005922
1	africans	0.0000000
2	africans	0.0000545
1	africas	0.0000000
2	africas	0.0001052
1	aft	0.0000216
2	aft	0.0000122
1	aftermath	0.0000150
2	aftermath	0.0000207
1	afternoon	0.0003453
2	afternoon	0.0001602
1	aftershocks	0.0000670
2	aftershocks	0.0000000
1	aftertax	0.0000335
2	aftertax	0.0000000
1	ag	0.0001061
2	ag	0.0000000
1	age	0.0002892
2	age	0.0002189
1	aged	0.0000502
2	aged	0.0000000
1	agencies	0.0001834
2	agencies	0.0001837
1	agency	0.0008044
2	agency	0.0008722
1	agencys	0.0001171
2	agencys	0.0000274
1	agenda	0.0000202
2	agenda	0.0000989
1	agent	0.0000407
2	agent	0.0002015
1	agents	0.0001123
2	agents	0.0002840
1	ages	0.0000768
2	ages	0.0000243
1	aggravated	0.0000156
2	aggravated	0.0000397
1	aggression	0.0000087
2	aggression	0.0000719
1	aggressive	0.0000903
2	aggressive	0.0000422
1	aggressively	0.0000328
2	aggressively	0.0000122
1	aging	0.0000211
2	aging	0.0000203
1	ago	0.0010652
2	ago	0.0008421
1	agree	0.0001068
2	agree	0.0002060
1	agreed	0.0004250
2	agreed	0.0006423
1	agreeing	0.0000000
2	agreeing	0.0000623
1	agreement	0.0006715
2	agreement	0.0010390
1	agreements	0.0001001
2	agreements	0.0000938
1	agrees	0.0000000
2	agrees	0.0000468
1	agricultural	0.0002842
2	agricultural	0.0000627
1	agriculture	0.0004999
2	agriculture	0.0000602
1	aground	0.0000447
2	aground	0.0000000
1	ahead	0.0002886
2	ahead	0.0001336
1	ahmed	0.0000001
2	ahmed	0.0000506
1	aid	0.0000476
2	aid	0.0010460
1	aide	0.0000000
2	aide	0.0002766
1	aided	0.0000072
2	aided	0.0000301
1	aides	0.0000000
2	aides	0.0002649
1	aiding	0.0000041
2	aiding	0.0000283
1	aids	0.0006623
2	aids	0.0000715
1	ailing	0.0000365
2	ailing	0.0000485
1	ailment	0.0000085
2	ailment	0.0000213
1	ailments	0.0000398
2	ailments	0.0000229
1	aim	0.0000319
2	aim	0.0000362
1	aimed	0.0000553
2	aimed	0.0002185
1	aiming	0.0000067
2	aiming	0.0000187
1	air	0.0021369
2	air	0.0002967
1	airborne	0.0000640
2	airborne	0.0000177
1	airbus	0.0000837
2	airbus	0.0000000
1	aircraft	0.0007256
2	aircraft	0.0000117
1	aired	0.0000000
2	aired	0.0000584
1	aires	0.0000060
2	aires	0.0000192
1	airline	0.0005188
2	airline	0.0000002
1	airliner	0.0000807
2	airliner	0.0000060
1	airlines	0.0006642
2	airlines	0.0000000
1	airplane	0.0001023
2	airplane	0.0000104
1	airplanes	0.0000558
2	airplanes	0.0000000
1	airport	0.0005100
2	airport	0.0001934
1	airports	0.0001144
2	airports	0.0000176
1	airspace	0.0000202
2	airspace	0.0000093
1	airways	0.0000652
2	airways	0.0000324
1	aisle	0.0000024
2	aisle	0.0000295
1	al	0.0000514
2	al	0.0000693
1	ala	0.0000420
2	ala	0.0000291
1	alabama	0.0000835
2	alabama	0.0001248
1	alamo	0.0000391
2	alamo	0.0000000
1	alan	0.0000675
2	alan	0.0001360
1	alarm	0.0000335
2	alarm	0.0000000
1	alaska	0.0001486
2	alaska	0.0000093
1	albania	0.0000000
2	albania	0.0000273
1	albany	0.0000335
2	albany	0.0000000
1	albert	0.0000184
2	albert	0.0001118
1	alberto	0.0000084
2	alberto	0.0000253
1	albrecht	0.0000167
2	albrecht	0.0000117
1	album	0.0000003
2	album	0.0000621
1	albuquerque	0.0000670
2	albuquerque	0.0000000
1	alcohol	0.0001513
2	alcohol	0.0000775
1	alcoholic	0.0000136
2	alcoholic	0.0000178
1	alert	0.0001289
2	alert	0.0000269
1	alerted	0.0000502
2	alerted	0.0000000
1	alex	0.0000001
2	alex	0.0000545
1	alexander	0.0000147
2	alexander	0.0000794
1	alexandria	0.0000210
2	alexandria	0.0000477
1	alfred	0.0000723
2	alfred	0.0000118
1	alfredo	0.0000000
2	alfredo	0.0000234
1	algeria	0.0000034
2	algeria	0.0000716
1	algerian	0.0000001
2	algerian	0.0000623
1	algerians	0.0000000
2	algerians	0.0000273
1	algerias	0.0000000
2	algerias	0.0000273
1	algiers	0.0000080
2	algiers	0.0000412
1	algotson	0.0000457
2	algotson	0.0000071
1	ali	0.0000009
2	ali	0.0001162
1	alice	0.0000343
2	alice	0.0000150
1	alien	0.0000013
2	alien	0.0000420
1	aliens	0.0000022
2	aliens	0.0001037
1	alike	0.0000566
2	alike	0.0000151
1	alito	0.0000000
2	alito	0.0000351
1	alive	0.0000527
2	alive	0.0001385
1	alkaline	0.0000335
2	alkaline	0.0000000
1	allan	0.0000306
2	allan	0.0000332
1	allegation	0.0000000
2	allegation	0.0000545
1	allegations	0.0000203
2	allegations	0.0002702
1	alleged	0.0000009
2	alleged	0.0005098
1	allegedly	0.0000434
2	allegedly	0.0001879
1	alleges	0.0000000
2	alleges	0.0000429
1	alleging	0.0000043
2	alleging	0.0000554
1	allen	0.0002004
2	allen	0.0000276
1	alliance	0.0000007
2	alliance	0.0001748
1	allied	0.0001087
2	allied	0.0000527
1	allies	0.0000114
2	allies	0.0002609
1	allmale	0.0000000
2	allmale	0.0000234
1	allocate	0.0000000
2	allocate	0.0000234
1	allocated	0.0000220
2	allocated	0.0000158
1	allow	0.0001439
2	allow	0.0005853
1	allowed	0.0001366
2	allowed	0.0005904
1	allowing	0.0001008
2	allowing	0.0001673
1	allows	0.0000690
2	allows	0.0001272
1	alltime	0.0000726
2	alltime	0.0000000
1	ally	0.0000000
2	ally	0.0001130
1	alongside	0.0000028
2	alongside	0.0000331
1	alpo	0.0000391
2	alpo	0.0000000
1	alter	0.0000161
2	alter	0.0000238
1	altered	0.0000461
2	altered	0.0000107
1	alternate	0.0000205
2	alternate	0.0000090
1	alternative	0.0000807
2	alternative	0.0001307
1	altitude	0.0000837
2	altitude	0.0000000
1	altogether	0.0000149
2	altogether	0.0000285
1	aluminum	0.0000670
2	aluminum	0.0000000
1	alvarez	0.0000000
2	alvarez	0.0000390
1	alzheimers	0.0000000
2	alzheimers	0.0000273
1	am	0.0006725
2	am	0.0005240
1	ama	0.0000300
2	ama	0.0000064
1	amal	0.0001340
2	amal	0.0000000
1	amalgam	0.0000000
2	amalgam	0.0000273
1	amateur	0.0000205
2	amateur	0.0000129
1	ambassador	0.0000000
2	ambassador	0.0002844
1	ambassadors	0.0000002
2	ambassadors	0.0000427
1	ambitious	0.0000171
2	ambitious	0.0000231
1	ambulance	0.0000502
2	ambulance	0.0000000
1	ambushed	0.0000099
2	ambushed	0.0000281
1	amended	0.0000000
2	amended	0.0000545
1	amendment	0.0000000
2	amendment	0.0002961
1	amendments	0.0000000
2	amendments	0.0000935
1	america	0.0005851
2	america	0.0005344
1	american	0.0020305
2	american	0.0016839
1	americans	0.0003154
2	americans	0.0005746
1	americas	0.0001455
2	americas	0.0001400
1	ames	0.0000892
2	ames	0.0000001
1	amid	0.0001790
2	amid	0.0000699
1	amirav	0.0000000
2	amirav	0.0000273
1	ammunition	0.0000428
2	ammunition	0.0000364
1	amnesty	0.0000000
2	amnesty	0.0001597
1	amount	0.0004246
2	amount	0.0001673
1	amounted	0.0000408
2	amounted	0.0000261
1	amounts	0.0000966
2	amounts	0.0000690
1	amritsar	0.0000346
2	amritsar	0.0000031
1	amtrak	0.0000502
2	amtrak	0.0000000
1	amy	0.0000000
2	amy	0.0000234
1	analysis	0.0000914
2	analysis	0.0000570
1	analyst	0.0004521
2	analyst	0.0000117
1	analysts	0.0010876
2	analysts	0.0000006
1	analyzing	0.0000287
2	analyzing	0.0000072
1	anasazi	0.0000502
2	anasazi	0.0000000
1	anatoly	0.0000000
2	anatoly	0.0000468
1	anc	0.0000000
2	anc	0.0001792
1	anchor	0.0000278
2	anchor	0.0000196
1	anchorage	0.0000447
2	anchorage	0.0000000
1	ancient	0.0000551
2	ancient	0.0000317
1	andean	0.0000000
2	andean	0.0000234
1	anderson	0.0000608
2	anderson	0.0001446
1	andersons	0.0000000
2	andersons	0.0000312
1	andre	0.0000000
2	andre	0.0000351
1	andreas	0.0000096
2	andreas	0.0000751
1	andrei	0.0000000
2	andrei	0.0000312
1	andreotti	0.0000000
2	andreotti	0.0000273
1	andrew	0.0001480
2	andrew	0.0000603
1	andrews	0.0000489
2	andrews	0.0000087
1	andy	0.0000391
2	andy	0.0000000
1	angeles	0.0005392
2	angeles	0.0002041
1	angels	0.0000022
2	angels	0.0000258
1	anger	0.0000192
2	anger	0.0000567
1	angered	0.0000116
2	angered	0.0000542
1	angle	0.0000335
2	angle	0.0000000
1	anglo	0.0000165
2	anglo	0.0000118
1	angola	0.0000000
2	angola	0.0001403
1	angolan	0.0000000
2	angolan	0.0000701
1	angolas	0.0000000
2	angolas	0.0000234
1	angry	0.0000010
2	angry	0.0001045
1	anguish	0.0000000
2	anguish	0.0000234
1	animal	0.0001897
2	animal	0.0000000
1	animals	0.0003405
2	animals	0.0000000
1	anita	0.0000000
2	anita	0.0000273
1	ann	0.0000484
2	ann	0.0000675
1	anne	0.0000088
2	anne	0.0000562
1	annexation	0.0000000
2	annexation	0.0000351
1	annexed	0.0000000
2	annexed	0.0000429
1	annie	0.0000057
2	annie	0.0000272
1	anniversary	0.0000408
2	anniversary	0.0001819
1	announce	0.0000247
2	announce	0.0000568
1	announced	0.0009247
2	announced	0.0006285
1	announcement	0.0002585
2	announcement	0.0002053
1	announcements	0.0000115
2	announcements	0.0000192
1	announcing	0.0000179
2	announcing	0.0000732
1	annual	0.0007364
2	annual	0.0001249
1	annually	0.0001513
2	annually	0.0000269
1	annunzio	0.0000000
2	annunzio	0.0000740
1	annunzios	0.0000000
2	annunzios	0.0000351
1	anonymity	0.0002365
2	anonymity	0.0004115
1	anonymous	0.0000596
2	anonymous	0.0000324
1	answer	0.0000577
2	answer	0.0002364
1	answered	0.0000240
2	answered	0.0000728
1	answering	0.0000242
2	answering	0.0000376
1	answers	0.0000154
2	answers	0.0000672
1	anthony	0.0000894
2	anthony	0.0000701
1	anthrax	0.0000331
2	anthrax	0.0000003
1	antiabortion	0.0000000
2	antiabortion	0.0000740
1	antiapartheid	0.0000000
2	antiapartheid	0.0000896
1	anticipate	0.0000278
2	anticipate	0.0000079
1	anticipated	0.0000636
2	anticipated	0.0000258
1	anticipation	0.0000610
2	anticipation	0.0000042
1	anticommunist	0.0000000
2	anticommunist	0.0000273
1	antidrug	0.0000064
2	antidrug	0.0000501
1	antigovernment	0.0000002
2	antigovernment	0.0000817
1	antimissile	0.0000071
2	antimissile	0.0000184
1	antisemitism	0.0000000
2	antisemitism	0.0000701
1	antismoking	0.0000335
2	antismoking	0.0000000
1	antiterrorist	0.0000000
2	antiterrorist	0.0000429
1	antitrust	0.0000457
2	antitrust	0.0000071
1	antonin	0.0000000
2	antonin	0.0000234
1	antonio	0.0000813
2	antonio	0.0001108
1	anwar	0.0000001
2	anwar	0.0000233
1	anxiety	0.0000261
2	anxiety	0.0000168
1	anxious	0.0000001
2	anxious	0.0000389
1	anybody	0.0000192
2	anybody	0.0001190
1	anymore	0.0000356
2	anymore	0.0000219
1	anyway	0.0000280
2	anyway	0.0000545
1	aon	0.0000000
2	aon	0.0000273
1	aoun	0.0001674
2	aoun	0.0000000
1	aouns	0.0001451
2	aouns	0.0000000
1	ap	0.0000346
2	ap	0.0001629
1	apart	0.0000620
2	apart	0.0000697
1	apartheid	0.0000000
2	apartheid	0.0002104
1	apartment	0.0002187
2	apartment	0.0001240
1	apartments	0.0000742
2	apartments	0.0000222
1	apiece	0.0000446
2	apiece	0.0000000
1	apollo	0.0000391
2	apollo	0.0000000
1	apologize	0.0000000
2	apologize	0.0000312
1	apologized	0.0000000
2	apologized	0.0000428
1	apology	0.0000000
2	apology	0.0000468
1	apparel	0.0000391
2	apparel	0.0000000
1	apparent	0.0001166
2	apparent	0.0000472
1	apparently	0.0003387
2	apparently	0.0002233
1	appeal	0.0000161
2	appeal	0.0003940
1	appealed	0.0000264
2	appealed	0.0000985
1	appealing	0.0000044
2	appealing	0.0000749
1	appeals	0.0000000
2	appeals	0.0003740
1	appear	0.0000701
2	appear	0.0002160
1	appearance	0.0000218
2	appearance	0.0001406
1	appearances	0.0000000
2	appearances	0.0000857
1	appeared	0.0002898
2	appeared	0.0003977
1	appearing	0.0000124
2	appearing	0.0000693
1	appears	0.0000973
2	appears	0.0001269
1	applauded	0.0000095
2	applauded	0.0000284
1	applause	0.0000000
2	applause	0.0000429
1	apple	0.0001340
2	apple	0.0000000
1	apples	0.0000781
2	apples	0.0000000
1	appliances	0.0000558
2	appliances	0.0000000
1	applicants	0.0000516
2	applicants	0.0000380
1	application	0.0000087
2	application	0.0000290
1	applications	0.0000808
2	applications	0.0000371
1	applied	0.0000591
2	applied	0.0000912
1	applies	0.0000016
2	applies	0.0000495
1	apply	0.0000370
2	apply	0.0001339
1	applying	0.0000209
2	applying	0.0000127
1	appoint	0.0000000
2	appoint	0.0000468
1	appointed	0.0000203
2	appointed	0.0001728
1	appointment	0.0000001
2	appointment	0.0000662
1	appointments	0.0000000
2	appointments	0.0000234
1	appreciated	0.0000079
2	appreciated	0.0000218
1	appreciation	0.0000238
2	appreciation	0.0000341
1	approach	0.0001296
2	approach	0.0001160
1	approached	0.0000728
2	approached	0.0000427
1	approaches	0.0000452
2	approaches	0.0000230
1	approaching	0.0000520
2	approaching	0.0000105
1	appropriate	0.0000655
2	appropriate	0.0000907
1	appropriations	0.0000000
2	appropriations	0.0001091
1	approval	0.0001679
2	approval	0.0002997
1	approve	0.0000136
2	approve	0.0001463
1	approved	0.0002252
2	approved	0.0004817
1	approving	0.0000000
2	approving	0.0000389
1	approximately	0.0000471
2	approximately	0.0000295
1	april	0.0007972
2	april	0.0006513
1	aquino	0.0000101
2	aquino	0.0001410
1	aquinos	0.0000000
2	aquinos	0.0000234
1	arab	0.0000097
2	arab	0.0004413
1	arabia	0.0000506
2	arabia	0.0003465
1	arabian	0.0000502
2	arabian	0.0000000
1	arabs	0.0000002
2	arabs	0.0001245
1	arafat	0.0000000
2	arafat	0.0001169
1	arafats	0.0000000
2	arafats	0.0000351
1	arbitration	0.0000000
2	arbitration	0.0000857
1	arc	0.0000000
2	arc	0.0000273
1	archaeologists	0.0000670
2	archaeologists	0.0000000
1	archbishop	0.0000000
2	archbishop	0.0000818
1	archdiocese	0.0000000
2	archdiocese	0.0000312
1	archer	0.0000000
2	archer	0.0000234
1	architect	0.0000310
2	architect	0.0000095
1	architects	0.0000007
2	architects	0.0000268
1	architecture	0.0000422
2	architecture	0.0000134
1	archive	0.0000000
2	archive	0.0000467
1	archives	0.0000055
2	archives	0.0000274
1	arco	0.0001227
2	arco	0.0000001
1	arctic	0.0000391
2	arctic	0.0000000
1	area	0.0013714
2	area	0.0002310
1	areas	0.0005544
2	areas	0.0002598
1	arena	0.0000000
2	arena	0.0000545
1	arent	0.0000969
2	arent	0.0000804
1	argentina	0.0000380
2	argentina	0.0000631
1	argue	0.0000000
2	argue	0.0000779
1	argued	0.0000118
2	argued	0.0002177
1	arguing	0.0000104
2	arguing	0.0000512
1	argument	0.0000000
2	argument	0.0001091
1	arguments	0.0000021
2	arguments	0.0001817
1	arising	0.0000189
2	arising	0.0000258
1	aristide	0.0000000
2	aristide	0.0001052
1	aristides	0.0000000
2	aristides	0.0000273
1	ariz	0.0000680
2	ariz	0.0000071
1	arizona	0.0001438
2	arizona	0.0000867
1	ark	0.0000447
2	ark	0.0000000
1	arkansas	0.0000724
2	arkansas	0.0000508
1	arm	0.0001136
2	arm	0.0000727
1	armed	0.0000402
2	armed	0.0004706
1	armenia	0.0000000
2	armenia	0.0000779
1	armenian	0.0000001
2	armenian	0.0000934
1	armenians	0.0000001
2	armenians	0.0000311
1	armies	0.0000000
2	armies	0.0000273
1	armor	0.0000001
2	armor	0.0000233
1	armored	0.0000148
2	armored	0.0000871
1	armory	0.0000402
2	armory	0.0000031
1	arms	0.0000148
2	arms	0.0004767
1	armstrong	0.0000260
2	armstrong	0.0000208
1	army	0.0002622
2	army	0.0010481
1	armys	0.0000127
2	armys	0.0000457
1	arnold	0.0000002
2	arnold	0.0000233
1	arose	0.0000121
2	arose	0.0000344
1	arraignment	0.0000003
2	arraignment	0.0000310
1	arrange	0.0000501
2	arrange	0.0000234
1	arranged	0.0000383
2	arranged	0.0000785
1	arrangement	0.0000555
2	arrangement	0.0000430
1	arrangements	0.0000270
2	arrangements	0.0000669
1	array	0.0000427
2	array	0.0000169
1	arrest	0.0000653
2	arrest	0.0003479
1	arrested	0.0002849
2	arrested	0.0006231
1	arresting	0.0000089
2	arresting	0.0000289
1	arrests	0.0000719
2	arrests	0.0001602
1	arrival	0.0000254
2	arrival	0.0000913
1	arrive	0.0000857
2	arrive	0.0000493
1	arrived	0.0002697
2	arrived	0.0002403
1	arriving	0.0000474
2	arriving	0.0000760
1	arsenal	0.0000086
2	arsenal	0.0000407
1	arsenals	0.0000000
2	arsenals	0.0000234
1	arson	0.0000073
2	arson	0.0000222
1	art	0.0003278
2	art	0.0001218
1	arthritis	0.0001097
2	arthritis	0.0000052
1	arthur	0.0001341
2	arthur	0.0001051
1	article	0.0000012
2	article	0.0001278
1	articles	0.0000202
2	articles	0.0000716
1	artifacts	0.0000949
2	artifacts	0.0000000
1	artificial	0.0000644
2	artificial	0.0000447
1	artillery	0.0000178
2	artillery	0.0000421
1	artist	0.0000357
2	artist	0.0000257
1	artistic	0.0000000
2	artistic	0.0000312
1	artists	0.0000307
2	artists	0.0001033
1	arts	0.0000299
2	arts	0.0001389
1	arturo	0.0000000
2	arturo	0.0000273
1	asbestos	0.0000000
2	asbestos	0.0001091
1	ash	0.0001339
2	ash	0.0000000
1	ashare	0.0000255
2	ashare	0.0000056
1	ashe	0.0000415
2	ashe	0.0000022
1	ashland	0.0000000
2	ashland	0.0000389
1	ashore	0.0000614
2	ashore	0.0000000
1	asia	0.0000007
2	asia	0.0001319
1	asian	0.0000950
2	asian	0.0000817
1	aside	0.0000322
2	aside	0.0001100
1	ask	0.0000562
2	ask	0.0003153
1	asked	0.0001886
2	asked	0.0015592
1	asking	0.0000331
2	asking	0.0002885
1	asks	0.0000145
2	asks	0.0000561
1	asleep	0.0000274
2	asleep	0.0000198
1	aspect	0.0000018
2	aspect	0.0000221
1	aspects	0.0000000
2	aspects	0.0000857
1	aspen	0.0000000
2	aspen	0.0000234
1	aspin	0.0000000
2	aspin	0.0000701
1	aspirin	0.0001691
2	aspirin	0.0000106
1	assad	0.0000081
2	assad	0.0000177
1	assailant	0.0000078
2	assailant	0.0000218
1	assailants	0.0000124
2	assailants	0.0000186
1	assassinate	0.0000000
2	assassinate	0.0000234
1	assassinated	0.0000012
2	assassinated	0.0000381
1	assassination	0.0000002
2	assassination	0.0000894
1	assassinations	0.0000000
2	assassinations	0.0000351
1	assault	0.0000483
2	assault	0.0002156
1	assaulted	0.0000012
2	assaulted	0.0000459
1	assaulting	0.0000002
2	assaulting	0.0000233
1	assaults	0.0000114
2	assaults	0.0000349
1	assemble	0.0000445
2	assemble	0.0000001
1	assembled	0.0000486
2	assembled	0.0000167
1	assembly	0.0001080
2	assembly	0.0002636
1	assertion	0.0000000
2	assertion	0.0000273
1	assertions	0.0000136
2	assertions	0.0000139
1	assess	0.0000403
2	assess	0.0000147
1	assessed	0.0000214
2	assessed	0.0000123
1	assessment	0.0000700
2	assessment	0.0000135
1	asset	0.0000816
2	asset	0.0000209
1	assets	0.0005013
2	assets	0.0000865
1	assigned	0.0000478
2	assigned	0.0000680
1	assignment	0.0000034
2	assignment	0.0000677
1	assignments	0.0000000
2	assignments	0.0000234
1	assist	0.0000309
2	assist	0.0000407
1	assistance	0.0000850
2	assistance	0.0002485
1	assistant	0.0001760
2	assistant	0.0003641
1	assistants	0.0000090
2	assistants	0.0000210
1	assisted	0.0000150
2	assisted	0.0000129
1	assisting	0.0000000
2	assisting	0.0000234
1	associate	0.0000542
2	associate	0.0001025
1	associated	0.0001501
2	associated	0.0002731
1	associates	0.0001311
2	associates	0.0000527
1	association	0.0007430
2	association	0.0002957
1	associations	0.0000627
2	associations	0.0000498
1	assume	0.0000739
2	assume	0.0000341
1	assumed	0.0000000
2	assumed	0.0000662
1	assuming	0.0000185
2	assuming	0.0000144
1	assurance	0.0000111
2	assurance	0.0000196
1	assurances	0.0000148
2	assurances	0.0000715
1	assure	0.0000334
2	assure	0.0000741
1	assured	0.0000089
2	assured	0.0001068
1	astronauts	0.0001284
2	astronauts	0.0000000
1	asylum	0.0000000
2	asylum	0.0000896
1	asylumseekers	0.0000000
2	asylumseekers	0.0000234
1	athens	0.0000614
2	athens	0.0000000
1	athletes	0.0000000
2	athletes	0.0000779
1	athletic	0.0000000
2	athletic	0.0000468
1	atkins	0.0000000
2	atkins	0.0000467
1	atlanta	0.0001723
2	atlanta	0.0001797
1	atlantabased	0.0000391
2	atlantabased	0.0000000
1	atlantic	0.0002452
2	atlantic	0.0000548
1	atlantis	0.0000726
2	atlantis	0.0000000
1	atlarge	0.0000000
2	atlarge	0.0000312
1	atmosphere	0.0001363
2	atmosphere	0.0000607
1	atomic	0.0000331
2	atomic	0.0000081
1	atop	0.0000152
2	atop	0.0000245
1	atrocities	0.0000000
2	atrocities	0.0000429
1	att	0.0002116
2	att	0.0000003
1	attached	0.0000654
2	attached	0.0000362
1	attack	0.0002905
2	attack	0.0006699
1	attacked	0.0000143
2	attacked	0.0002667
1	attackers	0.0000893
2	attackers	0.0000000
1	attacking	0.0000250
2	attacking	0.0000293
1	attacks	0.0000533
2	attacks	0.0003135
1	attempt	0.0001412
2	attempt	0.0003612
1	attempted	0.0000592
2	attempted	0.0001184
1	attempting	0.0000745
2	attempting	0.0000298
1	attempts	0.0000432
2	attempts	0.0000984
1	attend	0.0000000
2	attend	0.0002376
1	attendance	0.0000146
2	attendance	0.0000132
1	attendant	0.0000391
2	attendant	0.0000000
1	attendants	0.0001113
2	attendants	0.0000002
1	attended	0.0000000
2	attended	0.0002377
1	attending	0.0000195
2	attending	0.0001344
1	attention	0.0001785
2	attention	0.0002416
1	attire	0.0000129
2	attire	0.0000144
1	attitude	0.0000457
2	attitude	0.0000927
1	attitudes	0.0000001
2	attitudes	0.0000428
1	attorney	0.0000070
2	attorney	0.0013821
1	attorneys	0.0000194
2	attorneys	0.0003683
1	attract	0.0000805
2	attract	0.0000334
1	attracted	0.0000535
2	attracted	0.0000367
1	attraction	0.0000000
2	attraction	0.0000312
1	attractive	0.0000755
2	attractive	0.0000018
1	attributed	0.0002314
2	attributed	0.0000332
1	attrition	0.0000233
2	attrition	0.0000071
1	atts	0.0000502
2	atts	0.0000000
1	atwater	0.0000000
2	atwater	0.0000312
1	au	0.0000000
2	au	0.0000351
1	auburn	0.0000176
2	auburn	0.0000111
1	auction	0.0002288
2	auction	0.0000000
1	audience	0.0000247
2	audience	0.0001815
1	audiences	0.0000186
2	audiences	0.0000182
1	audit	0.0000349
2	audit	0.0000185
1	auditorium	0.0000070
2	auditorium	0.0000224
1	audits	0.0000447
2	audits	0.0000000
1	audubon	0.0000391
2	audubon	0.0000000
1	aug	0.0002842
2	aug	0.0003821
1	august	0.0005043
2	august	0.0002207
1	austerity	0.0000192
2	austerity	0.0000489
1	austin	0.0000688
2	austin	0.0000299
1	australia	0.0001572
2	australia	0.0000422
1	australian	0.0000878
2	australian	0.0000322
1	austria	0.0000093
2	austria	0.0000247
1	authentic	0.0000308
2	authentic	0.0000019
1	author	0.0000003
2	author	0.0001322
1	authoritarian	0.0000007
2	authoritarian	0.0000385
1	authorities	0.0007622
2	authorities	0.0007186
1	authority	0.0002077
2	authority	0.0002485
1	authorization	0.0000090
2	authorization	0.0000210
1	authorize	0.0000182
2	authorize	0.0000223
1	authorized	0.0000672
2	authorized	0.0000738
1	authorizing	0.0000110
2	authorizing	0.0000469
1	authors	0.0000004
2	authors	0.0000426
1	auto	0.0002906
2	auto	0.0000270
1	automaker	0.0000502
2	automaker	0.0000000
1	automakers	0.0000837
2	automakers	0.0000000
1	automatic	0.0001298
2	automatic	0.0000068
1	automatically	0.0000456
2	automatically	0.0000149
1	automobile	0.0000825
2	automobile	0.0000203
1	automobiles	0.0000669
2	automobiles	0.0000000
1	automotive	0.0000335
2	automotive	0.0000000
1	autonomy	0.0000000
2	autonomy	0.0000545
1	autopsy	0.0000670
2	autopsy	0.0000000
1	autumn	0.0000335
2	autumn	0.0000000
1	auxiliary	0.0000168
2	auxiliary	0.0000155
1	availability	0.0000213
2	availability	0.0000202
1	available	0.0005505
2	available	0.0001378
1	avalanche	0.0000736
2	avalanche	0.0000032
1	avenue	0.0000576
2	avenue	0.0000221
1	average	0.0013452
2	average	0.0000000
1	averaged	0.0001198
2	averaged	0.0000021
1	averages	0.0000502
2	averages	0.0000000
1	averaging	0.0000558
2	averaging	0.0000000
1	avery	0.0000000
2	avery	0.0000351
1	aviation	0.0002551
2	aviation	0.0000090
1	aviv	0.0000125
2	aviv	0.0000419
1	avoid	0.0001731
2	avoid	0.0001714
1	avoided	0.0000309
2	avoided	0.0000642
1	avoiding	0.0000152
2	avoiding	0.0000283
1	avril	0.0000000
2	avril	0.0000740
1	await	0.0000088
2	await	0.0000172
1	awaiting	0.0000869
2	awaiting	0.0000679
1	awakened	0.0000297
2	awakened	0.0000027
1	award	0.0000137
2	award	0.0002904
1	awarded	0.0000619
2	awarded	0.0001009
1	awards	0.0000677
2	awards	0.0001748
1	aware	0.0000948
2	aware	0.0001130
1	awareness	0.0000120
2	awareness	0.0000422
1	away	0.0008305
2	away	0.0004332
1	awful	0.0000231
2	awful	0.0000306
1	ayatollah	0.0000000
2	ayatollah	0.0000234
1	azcona	0.0000002
2	azcona	0.0000232
1	azerbaijan	0.0000000
2	azerbaijan	0.0001208
1	azerbaijani	0.0000000
2	azerbaijani	0.0000234
1	azerbaijanis	0.0000013
2	azerbaijanis	0.0000225
1	aziz	0.0000000
2	aziz	0.0000468
1	b	0.0001895
2	b	0.0002573
1	babbitt	0.0000000
2	babbitt	0.0000234
1	babies	0.0000862
2	babies	0.0000216
1	baby	0.0000719
2	baby	0.0001758
1	back	0.0012513
2	back	0.0010278
1	backed	0.0000482
2	backed	0.0001339
1	backers	0.0000021
2	backers	0.0000375
1	background	0.0000146
2	background	0.0000639
1	backing	0.0000118
2	backing	0.0000969
1	backlog	0.0000446
2	backlog	0.0000000
1	backs	0.0000172
2	backs	0.0000192
1	backup	0.0000910
2	backup	0.0000027
1	backward	0.0000263
2	backward	0.0000089
1	bacteria	0.0000558
2	bacteria	0.0000000
1	bad	0.0002892
2	bad	0.0001917
1	badly	0.0000736
2	badly	0.0000577
1	bag	0.0000390
2	bag	0.0000468
1	baggage	0.0000335
2	baggage	0.0000000
1	baghdad	0.0000001
2	baghdad	0.0001830
1	bags	0.0000837
2	bags	0.0000195
1	baguio	0.0000391
2	baguio	0.0000000
1	bahamas	0.0000359
2	bahamas	0.0000139
1	bahrain	0.0000209
2	bahrain	0.0000127
1	bail	0.0000290
2	bail	0.0001200
1	bailey	0.0000032
2	bailey	0.0000250
1	bailout	0.0000545
2	bailout	0.0000321
1	bain	0.0000447
2	bain	0.0000000
1	baker	0.0000353
2	baker	0.0004935
1	bakers	0.0000000
2	bakers	0.0000273
1	bakker	0.0000000
2	bakker	0.0000740
1	bakkers	0.0000000
2	bakkers	0.0000234
1	baku	0.0000007
2	baku	0.0000697
1	balance	0.0001395
2	balance	0.0000390
1	balanced	0.0000263
2	balanced	0.0000323
1	balcony	0.0000603
2	balcony	0.0000086
1	baldwin	0.0000502
2	baldwin	0.0000000
1	balked	0.0000061
2	balked	0.0000192
1	ball	0.0000267
2	ball	0.0000359
1	ballet	0.0000574
2	ballet	0.0000145
1	ballistic	0.0000000
2	ballistic	0.0000429
1	balloon	0.0000000
2	balloon	0.0000234
1	balloons	0.0000248
2	balloons	0.0000100
1	ballot	0.0000000
2	ballot	0.0001403
1	balloting	0.0000000
2	balloting	0.0000623
1	ballots	0.0000000
2	ballots	0.0000623
1	ballroom	0.0000497
2	ballroom	0.0000004
1	baltic	0.0000000
2	baltic	0.0001441
1	baltics	0.0000000
2	baltics	0.0000273
1	baltimore	0.0001257
2	baltimore	0.0000875
1	ban	0.0000413
2	ban	0.0003608
1	banana	0.0000391
2	banana	0.0000000
1	banca	0.0000391
2	banca	0.0000000
1	band	0.0001810
2	band	0.0000996
1	bands	0.0000238
2	bands	0.0000106
1	bangkok	0.0000335
2	bangkok	0.0000000
1	bangladesh	0.0000949
2	bangladesh	0.0000000
1	bank	0.0016702
2	bank	0.0002913
1	banker	0.0000000
2	banker	0.0000468
1	bankers	0.0001618
2	bankers	0.0000001
1	banking	0.0002063
2	banking	0.0000820
1	bankrupt	0.0000260
2	bankrupt	0.0000208
1	bankruptcy	0.0003457
2	bankruptcy	0.0000158
1	banks	0.0007873
2	banks	0.0000310
1	banned	0.0000332
2	banned	0.0001522
1	banner	0.0000272
2	banner	0.0000511
1	banning	0.0000120
2	banning	0.0000384
1	bans	0.0000000
2	bans	0.0000273
1	baptist	0.0000015
2	baptist	0.0000574
1	bar	0.0000446
2	bar	0.0001637
1	barahona	0.0000416
2	barahona	0.0000060
1	barash	0.0000000
2	barash	0.0000351
1	barbara	0.0000721
2	barbara	0.0000783
1	barbershop	0.0000391
2	barbershop	0.0000000
1	barboza	0.0000000
2	barboza	0.0000312
1	barcelona	0.0000102
2	barcelona	0.0000240
1	bare	0.0000174
2	bare	0.0000307
1	barely	0.0000470
2	barely	0.0000179
1	bargain	0.0000324
2	bargain	0.0000592
1	bargainers	0.0000033
2	bargainers	0.0000211
1	bargaining	0.0000901
2	bargaining	0.0000656
1	bargains	0.0000335
2	bargains	0.0000000
1	barnard	0.0000257
2	barnard	0.0000600
1	barney	0.0000680
2	barney	0.0000110
1	baron	0.0000052
2	baron	0.0000431
1	barr	0.0000136
2	barr	0.0000450
1	barracks	0.0000725
2	barracks	0.0000000
1	barrage	0.0000202
2	barrage	0.0000132
1	barred	0.0000000
2	barred	0.0001169
1	barrel	0.0002846
2	barrel	0.0000000
1	barrels	0.0001172
2	barrels	0.0000000
1	barrett	0.0000000
2	barrett	0.0000273
1	barrier	0.0000283
2	barrier	0.0000309
1	barriers	0.0000134
2	barriers	0.0001075
1	barring	0.0000000
2	barring	0.0000545
1	barry	0.0000146
2	barry	0.0001885
1	barrys	0.0000000
2	barrys	0.0000468
1	bars	0.0000177
2	bars	0.0000812
1	bartholomew	0.0000000
2	bartholomew	0.0000234
1	base	0.0005604
2	base	0.0001699
1	baseball	0.0001072
2	baseball	0.0000499
1	based	0.0006411
2	based	0.0002421
1	basement	0.0000495
2	basement	0.0000083
1	basements	0.0000340
2	basements	0.0000035
1	bases	0.0000606
2	bases	0.0000980
1	basic	0.0000486
2	basic	0.0001531
1	basically	0.0000778
2	basically	0.0000509
1	basin	0.0000935
2	basin	0.0000088
1	basis	0.0001837
2	basis	0.0001835
1	basketball	0.0000313
2	basketball	0.0000210
1	basra	0.0000090
2	basra	0.0000288
1	bass	0.0000349
2	bass	0.0000068
1	bastion	0.0000000
2	bastion	0.0000234
1	batalla	0.0000446
2	batalla	0.0000000
1	batch	0.0000006
2	batch	0.0000307
1	bath	0.0000335
2	bath	0.0000000
1	bathroom	0.0000499
2	bathroom	0.0000002
1	battered	0.0000335
2	battered	0.0000000
1	batteries	0.0000893
2	batteries	0.0000000
1	battery	0.0000550
2	battery	0.0000005
1	battle	0.0001910
2	battle	0.0001784
1	battled	0.0000210
2	battled	0.0000165
1	battles	0.0000088
2	battles	0.0000640
1	battling	0.0000300
2	battling	0.0000297
1	batus	0.0000000
2	batus	0.0000623
1	baucus	0.0000000
2	baucus	0.0000312
1	baugh	0.0000000
2	baugh	0.0000234
1	bay	0.0001697
2	bay	0.0000218
1	bb	0.0000642
2	bb	0.0000059
1	bc	0.0000777
2	bc	0.0000003
1	bcspehealth	0.0000000
2	bcspehealth	0.0000896
1	beach	0.0002299
2	beach	0.0000499
1	beaches	0.0000942
2	beaches	0.0000044
1	beams	0.0000229
2	beams	0.0000074
1	bean	0.0000335
2	bean	0.0000000
1	beans	0.0000558
2	beans	0.0000000
1	bear	0.0000841
2	bear	0.0000932
1	bearing	0.0000334
2	bearing	0.0000157
1	bearish	0.0000447
2	bearish	0.0000000
1	bears	0.0000107
2	bears	0.0000354
1	beat	0.0000462
2	beat	0.0001392
1	beaten	0.0000099
2	beaten	0.0000944
1	beating	0.0000176
2	beating	0.0000851
1	beautiful	0.0000115
2	beautiful	0.0000699
1	beazley	0.0000335
2	beazley	0.0000000
1	bechtel	0.0000420
2	bechtel	0.0000019
1	bed	0.0001293
2	bed	0.0000461
1	bedard	0.0000391
2	bedard	0.0000000
1	bedroom	0.0000289
2	bedroom	0.0000188
1	beds	0.0000330
2	beds	0.0000159
1	beef	0.0001671
2	beef	0.0000080
1	beer	0.0000816
2	beer	0.0000171
1	begin	0.0003294
2	begin	0.0002610
1	beginning	0.0003048
2	beginning	0.0002392
1	begins	0.0001335
2	begins	0.0000938
1	begun	0.0001086
2	begun	0.0000761
1	behalf	0.0000000
2	behalf	0.0002493
1	behavior	0.0000447
2	behavior	0.0000740
1	beijing	0.0000000
2	beijing	0.0001091
1	beings	0.0000083
2	beings	0.0000293
1	beirut	0.0003014
2	beirut	0.0000000
1	beita	0.0000390
2	beita	0.0000001
1	beleaguered	0.0000021
2	beleaguered	0.0000258
1	belfast	0.0000558
2	belfast	0.0000000
1	belgian	0.0000177
2	belgian	0.0000111
1	belgium	0.0000424
2	belgium	0.0000366
1	belgrade	0.0000502
2	belgrade	0.0000000
1	belief	0.0000382
2	belief	0.0000396
1	beliefs	0.0000099
2	beliefs	0.0000437
1	believe	0.0003706
2	believe	0.0005829
1	believed	0.0003728
2	believed	0.0002930
1	believes	0.0000512
2	believes	0.0001902
1	bell	0.0001498
2	bell	0.0000007
1	bella	0.0000000
2	bella	0.0000233
1	bellies	0.0000726
2	bellies	0.0000000
1	bells	0.0000237
2	bells	0.0000068
1	belmont	0.0000329
2	belmont	0.0000082
1	belong	0.0000130
2	belong	0.0000221
1	belonged	0.0000176
2	belonged	0.0000150
1	belonging	0.0000203
2	belonging	0.0000365
1	belongings	0.0000391
2	belongings	0.0000000
1	belongs	0.0000092
2	belongs	0.0000248
1	belt	0.0000781
2	belt	0.0000000
1	ben	0.0000129
2	ben	0.0000728
1	bench	0.0000000
2	bench	0.0000234
1	benchmark	0.0000447
2	benchmark	0.0000000
1	bend	0.0000234
2	bend	0.0000109
1	bendectin	0.0000000
2	bendectin	0.0000273
1	bender	0.0000440
2	bender	0.0000083
1	bendjedid	0.0000000
2	bendjedid	0.0000506
1	beneath	0.0000557
2	beneath	0.0000274
1	benedict	0.0000000
2	benedict	0.0000390
1	beneficiaries	0.0000208
2	beneficiaries	0.0000167
1	benefit	0.0001756
2	benefit	0.0001073
1	benefited	0.0000217
2	benefited	0.0000121
1	benefits	0.0001632
2	benefits	0.0002211
1	benjamin	0.0000000
2	benjamin	0.0000506
1	bennett	0.0000000
2	bennett	0.0001013
1	benson	0.0000059
2	benson	0.0000270
1	bent	0.0000007
2	bent	0.0000384
1	benton	0.0000354
2	benton	0.0000065
1	bentsen	0.0000000
2	bentsen	0.0003428
1	bergland	0.0000391
2	bergland	0.0000000
1	berkeley	0.0000093
2	berkeley	0.0000169
1	berlin	0.0000128
2	berlin	0.0002794
1	bermudez	0.0000000
2	bermudez	0.0000701
1	bernard	0.0000389
2	bernard	0.0000313
1	bernardino	0.0000391
2	bernardino	0.0000000
1	berrigan	0.0000000
2	berrigan	0.0000234
1	best	0.0003331
2	best	0.0005350
1	bestknown	0.0000376
2	bestknown	0.0000010
1	bet	0.0000275
2	bet	0.0000158
1	bethlehem	0.0000361
2	bethlehem	0.0000021
1	betrayed	0.0000000
2	betrayed	0.0000273
1	better	0.0003966
2	better	0.0004283
1	betty	0.0000130
2	betty	0.0000182
1	beverage	0.0000335
2	beverage	0.0000000
1	beverly	0.0000251
2	beverly	0.0000487
1	beyer	0.0000192
2	beyer	0.0000139
1	bias	0.0000010
2	bias	0.0000383
1	bible	0.0000000
2	bible	0.0000390
1	bid	0.0008355
2	bid	0.0000947
1	bidder	0.0000725
2	bidder	0.0000000
1	bidders	0.0000391
2	bidders	0.0000000
1	bidding	0.0000765
2	bidding	0.0000050
1	biden	0.0000000
2	biden	0.0000351
1	bids	0.0000855
2	bids	0.0000027
1	big	0.0010533
2	big	0.0001960
1	bigcity	0.0000000
2	bigcity	0.0000234
1	bigger	0.0000552
2	bigger	0.0000394
1	biggest	0.0004586
2	biggest	0.0001474
1	bigotry	0.0000000
2	bigotry	0.0000312
1	bill	0.0002024
2	bill	0.0013275
1	billboard	0.0000000
2	billboard	0.0000506
1	billed	0.0000125
2	billed	0.0000302
1	billion	0.0042679
2	billion	0.0003832
1	billions	0.0000856
2	billions	0.0000532
1	bills	0.0003122
2	bills	0.0002029
1	billy	0.0000000
2	billy	0.0000506
1	bilzerian	0.0000391
2	bilzerian	0.0000000
1	binding	0.0000057
2	binding	0.0000467
1	biography	0.0000000
2	biography	0.0000234
1	biological	0.0000552
2	biological	0.0000121
1	biomedical	0.0000003
2	biomedical	0.0000232
1	bipartisan	0.0000000
2	bipartisan	0.0000779
1	bird	0.0000848
2	bird	0.0000538
1	birds	0.0001619
2	birds	0.0000000
1	birendra	0.0000325
2	birendra	0.0000007
1	birmingham	0.0000111
2	birmingham	0.0000273
1	birth	0.0000838
2	birth	0.0001207
1	birthday	0.0000486
2	birthday	0.0000634
1	birthdays	0.0000000
2	birthdays	0.0000390
1	bishop	0.0000000
2	bishop	0.0001247
1	bishops	0.0000000
2	bishops	0.0001480
1	bit	0.0001435
2	bit	0.0000791
1	bite	0.0000283
2	bite	0.0000075
1	bites	0.0000404
2	bites	0.0000030
1	bits	0.0000335
2	bits	0.0000000
1	bitter	0.0000000
2	bitter	0.0000857
1	bitterly	0.0000000
2	bitterly	0.0000351
1	bizarre	0.0000057
2	bizarre	0.0000233
1	black	0.0001251
2	black	0.0011243
1	blackburn	0.0000391
2	blackburn	0.0000000
1	blacked	0.0000186
2	blacked	0.0000104
1	blackowned	0.0000502
2	blackowned	0.0000000
1	blacks	0.0000000
2	blacks	0.0002961
1	blame	0.0000571
2	blame	0.0001394
1	blamed	0.0002515
2	blamed	0.0001595
1	blanchard	0.0000000
2	blanchard	0.0000312
1	blank	0.0000001
2	blank	0.0000233
1	blanket	0.0000098
2	blanket	0.0000204
1	blankets	0.0000335
2	blankets	0.0000000
1	blast	0.0001340
2	blast	0.0000000
1	blasted	0.0000226
2	blasted	0.0000193
1	blasts	0.0000391
2	blasts	0.0000000
1	blaze	0.0001451
2	blaze	0.0000000
1	bleach	0.0000447
2	bleach	0.0000000
1	bleeding	0.0000332
2	bleeding	0.0000080
1	blessing	0.0000000
2	blessing	0.0000273
1	blew	0.0000447
2	blew	0.0000000
1	blier	0.0000000
2	blier	0.0000506
1	blind	0.0000187
2	blind	0.0000415
1	bloc	0.0000000
2	bloc	0.0001364
1	block	0.0001713
2	block	0.0001337
1	blockade	0.0000000
2	blockade	0.0000506
1	blockbuster	0.0000558
2	blockbuster	0.0000000
1	blocked	0.0000258
2	blocked	0.0000833
1	blocking	0.0000079
2	blocking	0.0000490
1	blocks	0.0001121
2	blocks	0.0000270
1	blocs	0.0000000
2	blocs	0.0000234
1	blood	0.0004377
2	blood	0.0000724
1	bloody	0.0000001
2	bloody	0.0000857
1	bloom	0.0000000
2	bloom	0.0001130
1	bloomberg	0.0000391
2	bloomberg	0.0000000
1	blow	0.0000504
2	blow	0.0000895
1	blown	0.0000361
2	blown	0.0000059
1	blue	0.0002778
2	blue	0.0000593
1	bluechip	0.0000447
2	bluechip	0.0000000
1	blueprint	0.0000000
2	blueprint	0.0000390
1	bnai	0.0000000
2	bnai	0.0000234
1	board	0.0014554
2	board	0.0005620
1	boarding	0.0000335
2	boarding	0.0000000
1	boards	0.0000274
2	boards	0.0000393
1	boat	0.0002596
2	boat	0.0000487
1	boats	0.0001284
2	boats	0.0000000
1	bob	0.0001974
2	bob	0.0002752
1	bobby	0.0000191
2	bobby	0.0000139
1	boca	0.0000313
2	boca	0.0000015
1	bodies	0.0002781
2	bodies	0.0001097
1	body	0.0002194
2	body	0.0003417
1	bodyguards	0.0000000
2	bodyguards	0.0000584
1	bodys	0.0000767
2	bodys	0.0000049
1	boeing	0.0003293
2	boeing	0.0000000
1	boesky	0.0000000
2	boesky	0.0000623
1	bofill	0.0000000
2	bofill	0.0000273
1	bogus	0.0000000
2	bogus	0.0000312
1	boharski	0.0000000
2	boharski	0.0000351
1	boise	0.0000416
2	boise	0.0000021
1	bolivia	0.0000000
2	bolivia	0.0000273
1	bolsheviks	0.0000000
2	bolsheviks	0.0000273
1	bolster	0.0000176
2	bolster	0.0000306
1	bolstered	0.0000374
2	bolstered	0.0000090
1	bolts	0.0000556
2	bolts	0.0000002
1	bomb	0.0002825
2	bomb	0.0000678
1	bomber	0.0000978
2	bomber	0.0000408
1	bombers	0.0000459
2	bombers	0.0000147
1	bombing	0.0000226
2	bombing	0.0001517
1	bombings	0.0000190
2	bombings	0.0000413
1	bombs	0.0002344
2	bombs	0.0000000
1	bond	0.0006980
2	bond	0.0000388
1	bondholders	0.0001005
2	bondholders	0.0000000
1	bonds	0.0004767
2	bonds	0.0000257
1	bone	0.0000131
2	bone	0.0000571
1	bones	0.0000036
2	bones	0.0000988
1	bonesmen	0.0000000
2	bonesmen	0.0000234
1	bongo	0.0000000
2	bongo	0.0000273
1	bonn	0.0000000
2	bonn	0.0000390
1	bono	0.0000000
2	bono	0.0000506
1	bonus	0.0000528
2	bonus	0.0000099
1	bonuses	0.0000502
2	bonuses	0.0000000
1	book	0.0000516
2	book	0.0003653
1	booked	0.0000630
2	booked	0.0000067
1	booking	0.0000335
2	booking	0.0000000
1	books	0.0000515
2	books	0.0001705
1	bookstore	0.0000187
2	bookstore	0.0000103
1	boom	0.0000933
2	boom	0.0000050
1	booming	0.0000348
2	booming	0.0000030
1	boost	0.0001562
2	boost	0.0000741
1	boosted	0.0000916
2	boosted	0.0000062
1	booster	0.0001172
2	booster	0.0000000
1	boosting	0.0000243
2	boosting	0.0000103
1	boots	0.0000475
2	boots	0.0000058
1	bordallo	0.0000000
2	bordallo	0.0000234
1	border	0.0001733
2	border	0.0003738
1	borders	0.0000085
2	borders	0.0000720
1	bored	0.0000320
2	bored	0.0000010
1	boren	0.0000000
2	boren	0.0000234
1	borer	0.0000335
2	borer	0.0000000
1	boring	0.0000192
2	boring	0.0000100
1	boris	0.0000000
2	boris	0.0000662
1	born	0.0000297
2	born	0.0002636
1	borough	0.0000453
2	borough	0.0000190
1	borrow	0.0000614
2	borrow	0.0000000
1	borrowed	0.0000430
2	borrowed	0.0000168
1	borrowers	0.0000155
2	borrowers	0.0000164
1	borrowing	0.0000580
2	borrowing	0.0000140
1	bosch	0.0000000
2	bosch	0.0000506
1	boschwitz	0.0000000
2	boschwitz	0.0000390
1	boss	0.0000404
2	boss	0.0000614
1	boston	0.0002237
2	boston	0.0002023
1	bostons	0.0000019
2	bostons	0.0000221
1	botha	0.0000000
2	botha	0.0000623
1	bothas	0.0000000
2	bothas	0.0000273
1	botswana	0.0000000
2	botswana	0.0000351
1	bottle	0.0000834
2	bottle	0.0000003
1	bottles	0.0000837
2	bottles	0.0000000
1	bottom	0.0001294
2	bottom	0.0000266
1	boucher	0.0000000
2	boucher	0.0000390
1	bought	0.0003789
2	bought	0.0000277
1	bound	0.0000957
2	bound	0.0000306
1	bounty	0.0000000
2	bounty	0.0000234
1	bourgeois	0.0000000
2	bourgeois	0.0000312
1	bow	0.0000029
2	bow	0.0000292
1	bowed	0.0000101
2	bowed	0.0000241
1	bowen	0.0000000
2	bowen	0.0000312
1	bowl	0.0000614
2	bowl	0.0000000
1	bowsher	0.0000000
2	bowsher	0.0000312
1	box	0.0001244
2	box	0.0000378
1	boxes	0.0000862
2	boxes	0.0000333
1	boxing	0.0000000
2	boxing	0.0000312
1	boy	0.0001364
2	boy	0.0001386
1	boycott	0.0000000
2	boycott	0.0000935
1	boycotted	0.0000000
2	boycotted	0.0000351
1	boyd	0.0000000
2	boyd	0.0000273
1	boyfriend	0.0000001
2	boyfriend	0.0000272
1	boys	0.0001941
2	boys	0.0000554
1	bradley	0.0000528
2	bradley	0.0001229
1	brady	0.0000361
2	brady	0.0000917
1	bragg	0.0000336
2	bragg	0.0000038
1	brain	0.0000727
2	brain	0.0000584
1	braking	0.0000335
2	braking	0.0000000
1	branch	0.0000320
2	branch	0.0000829
1	branches	0.0000364
2	branches	0.0000175
1	brand	0.0000517
2	brand	0.0000107
1	branded	0.0000067
2	branded	0.0000187
1	brando	0.0000576
2	brando	0.0000066
1	brands	0.0000726
2	brands	0.0000000
1	branover	0.0000391
2	branover	0.0000000
1	brave	0.0000000
2	brave	0.0000312
1	brawley	0.0000000
2	brawley	0.0000468
1	brazil	0.0000901
2	brazil	0.0000267
1	brazilian	0.0000755
2	brazilian	0.0000058
1	brazils	0.0000069
2	brazils	0.0000225
1	breach	0.0000212
2	breach	0.0000125
1	bread	0.0000702
2	bread	0.0000718
1	break	0.0001773
2	break	0.0002347
1	breakdown	0.0000210
2	breakdown	0.0000165
1	breakfast	0.0000001
2	breakfast	0.0000545
1	breakin	0.0000172
2	breakin	0.0000192
1	breaking	0.0000840
2	breaking	0.0000621
1	breaks	0.0000255
2	breaks	0.0000329
1	breakthrough	0.0000000
2	breakthrough	0.0000545
1	breast	0.0000900
2	breast	0.0000112
1	breath	0.0000142
2	breath	0.0000135
1	breathe	0.0000335
2	breathe	0.0000000
1	breathing	0.0000078
2	breathing	0.0000179
1	breeding	0.0000404
2	breeding	0.0000069
1	breeze	0.0000662
2	breeze	0.0000006
1	brenda	0.0000050
2	brenda	0.0000199
1	brennan	0.0000013
2	brennan	0.0000497
1	brent	0.0000252
2	brent	0.0000214
1	brian	0.0000647
2	brian	0.0000444
1	bribery	0.0000000
2	bribery	0.0000506
1	bribes	0.0000000
2	bribes	0.0000390
1	brick	0.0000197
2	brick	0.0000135
1	bricklin	0.0000000
2	bricklin	0.0000429
1	bridal	0.0000000
2	bridal	0.0000390
1	bride	0.0000066
2	bride	0.0000266
1	brides	0.0000000
2	brides	0.0000234
1	bridesmaids	0.0000000
2	bridesmaids	0.0000234
1	bridge	0.0001504
2	bridge	0.0000664
1	bridges	0.0000262
2	bridges	0.0000285
1	brief	0.0000888
2	brief	0.0002497
1	briefed	0.0000000
2	briefed	0.0000351
1	briefing	0.0000005
2	briefing	0.0000892
1	briefings	0.0000076
2	briefings	0.0000297
1	briefly	0.0000316
2	briefly	0.0000753
1	brig	0.0000064
2	brig	0.0000345
1	brigade	0.0000058
2	brigade	0.0000505
1	brigades	0.0000485
2	brigades	0.0000012
1	briggs	0.0000335
2	briggs	0.0000000
1	bright	0.0000533
2	bright	0.0000446
1	brilliant	0.0000150
2	brilliant	0.0000363
1	bring	0.0002654
2	bring	0.0003524
1	bringing	0.0001184
2	bringing	0.0001355
1	brings	0.0000520
2	brings	0.0000260
1	brinks	0.0000335
2	brinks	0.0000000
1	brisk	0.0000502
2	brisk	0.0000000
1	britain	0.0001683
2	britain	0.0003150
1	britains	0.0000684
2	britains	0.0000652
1	britannica	0.0000000
2	britannica	0.0000312
1	british	0.0007340
2	british	0.0002201
1	britons	0.0000128
2	britons	0.0000417
1	broad	0.0000872
2	broad	0.0000989
1	broadcast	0.0000578
2	broadcast	0.0003337
1	broadcasters	0.0000000
2	broadcasters	0.0000390
1	broadcasting	0.0000911
2	broadcasting	0.0000689
1	broadcasts	0.0000167
2	broadcasts	0.0000740
1	broaden	0.0000125
2	broaden	0.0000186
1	broader	0.0000641
2	broader	0.0000488
1	broadway	0.0000558
2	broadway	0.0000000
1	broke	0.0002262
2	broke	0.0002668
1	broken	0.0001386
2	broken	0.0000825
1	broker	0.0001228
2	broker	0.0000000
1	brokerage	0.0001228
2	brokerage	0.0000000
1	brokered	0.0000000
2	brokered	0.0000312
1	brokers	0.0001307
2	brokers	0.0000023
1	bronfman	0.0000000
2	bronfman	0.0000273
1	bronx	0.0000004
2	bronx	0.0000270
1	brooklyn	0.0000222
2	brooklyn	0.0000702
1	brooks	0.0000439
2	brooks	0.0000628
1	bros	0.0000000
2	bros	0.0000506
1	brothels	0.0000000
2	brothels	0.0000273
1	brother	0.0000489
2	brother	0.0002386
1	brothers	0.0001020
2	brothers	0.0001158
1	brought	0.0003390
2	brought	0.0003829
1	brown	0.0001427
2	brown	0.0001653
1	browning	0.0000170
2	browning	0.0000193
1	broyles	0.0000614
2	broyles	0.0000000
1	bruce	0.0000844
2	bruce	0.0000619
1	brush	0.0001228
2	brush	0.0000000
1	brushed	0.0000000
2	brushed	0.0000351
1	brussels	0.0000133
2	brussels	0.0000803
1	brutal	0.0000000
2	brutal	0.0000584
1	brutality	0.0000000
2	brutality	0.0000273
1	bryan	0.0000000
2	bryan	0.0000468
1	bryant	0.0000647
2	bryant	0.0000094
1	bst	0.0000030
2	bst	0.0000369
1	bubble	0.0000265
2	bubble	0.0000127
1	bubbles	0.0000285
2	bubbles	0.0000035
1	buchanan	0.0000000
2	buchanan	0.0000234
1	bucharest	0.0000001
2	bucharest	0.0000350
1	buckey	0.0000000
2	buckey	0.0000273
1	buckley	0.0000001
2	buckley	0.0000233
1	budapest	0.0000137
2	budapest	0.0000216
1	buddhist	0.0000558
2	buddhist	0.0000000
1	buddy	0.0000098
2	buddy	0.0000204
1	budget	0.0002566
2	budget	0.0009897
1	budgets	0.0000284
2	budgets	0.0000152
1	budvar	0.0000000
2	budvar	0.0000234
1	buenos	0.0000060
2	buenos	0.0000192
1	buffalo	0.0000642
2	buffalo	0.0000058
1	buffs	0.0000152
2	buffs	0.0000128
1	bug	0.0000335
2	bug	0.0000000
1	build	0.0002359
2	build	0.0001120
1	builder	0.0000335
2	builder	0.0000000
1	builders	0.0000615
2	builders	0.0000155
1	building	0.0006751
2	building	0.0003898
1	buildings	0.0003720
2	buildings	0.0000247
1	buildup	0.0000881
2	buildup	0.0000204
1	built	0.0004129
2	built	0.0000819
1	bujang	0.0000446
2	bujang	0.0000000
1	bulgaria	0.0000000
2	bulgaria	0.0000545
1	bulk	0.0000313
2	bulk	0.0000366
1	bull	0.0000558
2	bull	0.0000000
1	bullet	0.0000532
2	bullet	0.0000486
1	bulletin	0.0000305
2	bulletin	0.0000255
1	bullets	0.0000893
2	bullets	0.0000000
1	bullion	0.0001061
2	bullion	0.0000000
1	bullish	0.0000592
2	bullish	0.0000093
1	bumper	0.0000294
2	bumper	0.0000146
1	bunch	0.0000165
2	bunch	0.0000158
1	bundesbank	0.0000670
2	bundesbank	0.0000000
1	burbank	0.0000310
2	burbank	0.0000056
1	burden	0.0000947
2	burden	0.0001092
1	burdick	0.0000335
2	burdick	0.0000000
1	bureau	0.0002064
2	bureau	0.0000975
1	bureaucracy	0.0000070
2	bureaucracy	0.0000302
1	bureaucrats	0.0000019
2	bureaucrats	0.0000415
1	bureaus	0.0000142
2	bureaus	0.0000251
1	burgeoning	0.0000137
2	burgeoning	0.0000216
1	burger	0.0000335
2	burger	0.0000000
1	burglary	0.0000241
2	burglary	0.0000222
1	burgues	0.0000335
2	burgues	0.0000000
1	burial	0.0000499
2	burial	0.0000353
1	buried	0.0000324
2	buried	0.0000514
1	burke	0.0000558
2	burke	0.0000000
1	burleson	0.0000002
2	burleson	0.0000349
1	burlington	0.0000335
2	burlington	0.0000000
1	burma	0.0000670
2	burma	0.0000000
1	burmas	0.0000335
2	burmas	0.0000000
1	burn	0.0000967
2	burn	0.0000260
1	burned	0.0002555
2	burned	0.0000399
1	burnham	0.0000558
2	burnham	0.0000000
1	burning	0.0000820
2	burning	0.0000675
1	burns	0.0000596
2	burns	0.0000012
1	burst	0.0000739
2	burst	0.0000108
1	burt	0.0000073
2	burt	0.0000183
1	burton	0.0000525
2	burton	0.0000101
1	bury	0.0000280
2	bury	0.0000194
1	bus	0.0005833
2	bus	0.0000331
1	buses	0.0001721
2	buses	0.0000007
1	busfield	0.0000000
2	busfield	0.0000234
1	bush	0.0000008
2	bush	0.0036967
1	bushel	0.0003349
2	bushel	0.0000000
1	bushels	0.0001730
2	bushels	0.0000000
1	bushs	0.0000114
2	bushs	0.0006115
1	business	0.0018831
2	business	0.0003608
1	businesses	0.0005505
2	businesses	0.0000443
1	businessman	0.0000085
2	businessman	0.0000953
1	businessmen	0.0000138
2	businessmen	0.0000800
1	bust	0.0000447
2	bust	0.0000000
1	buster	0.0000158
2	buster	0.0000123
1	busy	0.0000529
2	busy	0.0000527
1	butcher	0.0000726
2	butcher	0.0000000
1	buthelezi	0.0000000
2	buthelezi	0.0000390
1	butterfly	0.0000614
2	butterfly	0.0000000
1	button	0.0000107
2	button	0.0000159
1	buttons	0.0000000
2	buttons	0.0000234
1	buy	0.0006398
2	buy	0.0001067
1	buyer	0.0000949
2	buyer	0.0000000
1	buyers	0.0002065
2	buyers	0.0000000
1	buying	0.0004618
2	buying	0.0000127
1	buyout	0.0002847
2	buyout	0.0000000
1	buyouts	0.0000614
2	buyouts	0.0000000
1	buys	0.0000272
2	buys	0.0000044
1	bypass	0.0000164
2	bypass	0.0000314
1	byrd	0.0000000
2	byrd	0.0000506
1	byrne	0.0000000
2	byrne	0.0000351
1	byzantine	0.0000099
2	byzantine	0.0000164
1	c	0.0002236
2	c	0.0001517
1	cabbage	0.0000427
2	cabbage	0.0000014
1	cabin	0.0000335
2	cabin	0.0000000
1	cabinet	0.0000096
2	cabinet	0.0003323
1	cable	0.0002003
2	cable	0.0000472
1	cafe	0.0000614
2	cafe	0.0000000
1	cain	0.0001005
2	cain	0.0000000
1	cairo	0.0000295
2	cairo	0.0000573
1	cake	0.0000335
2	cake	0.0000000
1	caledonia	0.0000040
2	caledonia	0.0000206
1	calero	0.0000000
2	calero	0.0000429
1	calgary	0.0000502
2	calgary	0.0000000
1	caliber	0.0000253
2	caliber	0.0000330
1	calif	0.0004284
2	calif	0.0001139
1	california	0.0009287
2	california	0.0002946
1	californians	0.0000447
2	californians	0.0000000
1	californias	0.0001231
2	californias	0.0000075
1	call	0.0003015
2	call	0.0005376
1	called	0.0006073
2	called	0.0013332
1	caller	0.0000411
2	caller	0.0000025
1	callers	0.0000307
2	callers	0.0000098
1	calling	0.0000615
2	calling	0.0003311
1	calls	0.0003025
2	calls	0.0004278
1	calm	0.0000138
2	calm	0.0000371
1	camarena	0.0000047
2	camarena	0.0000357
1	cambodia	0.0000000
2	cambodia	0.0000701
1	cambodian	0.0000000
2	cambodian	0.0000312
1	cambridge	0.0000340
2	cambridge	0.0000269
1	came	0.0010019
2	came	0.0007928
1	camera	0.0000119
2	camera	0.0000267
1	cameras	0.0000373
2	cameras	0.0000207
1	camp	0.0000231
2	camp	0.0002059
1	campaign	0.0000000
2	campaign	0.0017337
1	campaigned	0.0000000
2	campaigned	0.0000623
1	campaigning	0.0000000
2	campaigning	0.0001247
1	campaigns	0.0000000
2	campaigns	0.0001675
1	campbell	0.0001151
2	campbell	0.0000132
1	campeau	0.0000670
2	campeau	0.0000000
1	camped	0.0000228
2	camped	0.0000114
1	camps	0.0000191
2	camps	0.0001035
1	campus	0.0000000
2	campus	0.0001013
1	canada	0.0002964
2	canada	0.0000853
1	canadian	0.0003828
2	canadian	0.0000055
1	canal	0.0000482
2	canal	0.0000599
1	cancel	0.0000273
2	cancel	0.0000277
1	canceled	0.0000764
2	canceled	0.0001064
1	canceling	0.0000080
2	canceling	0.0000178
1	cancellation	0.0000236
2	cancellation	0.0000186
1	cancer	0.0002433
2	cancer	0.0001380
1	candidacy	0.0000000
2	candidacy	0.0001052
1	candidate	0.0000000
2	candidate	0.0004948
1	candidates	0.0000360
2	candidates	0.0004891
1	canning	0.0000447
2	canning	0.0000000
1	cannon	0.0000264
2	cannon	0.0000128
1	cano	0.0000000
2	cano	0.0000312
1	cans	0.0000670
2	cans	0.0000000
1	cant	0.0002926
2	cant	0.0003451
1	canyon	0.0000893
2	canyon	0.0000000
1	cap	0.0000278
2	cap	0.0000156
1	capabilities	0.0000445
2	capabilities	0.0000001
1	capability	0.0000104
2	capability	0.0000434
1	capable	0.0000558
2	capable	0.0000585
1	capacity	0.0001987
2	capacity	0.0000327
1	cape	0.0000335
2	cape	0.0000506
1	capita	0.0000295
2	capita	0.0000067
1	capital	0.0005111
2	capital	0.0006328
1	capitalism	0.0000075
2	capitalism	0.0000220
1	capitalist	0.0000000
2	capitalist	0.0000390
1	capitals	0.0000244
2	capitals	0.0000414
1	capitol	0.0000099
2	capitol	0.0001567
1	capped	0.0000153
2	capped	0.0000205
1	caps	0.0000225
2	caps	0.0000194
1	capt	0.0001263
2	capt	0.0000171
1	captain	0.0000899
2	captain	0.0000269
1	captive	0.0000010
2	captive	0.0000460
1	captives	0.0000041
2	captives	0.0000283
1	captivity	0.0000129
2	captivity	0.0000221
1	capture	0.0000457
2	capture	0.0000616
1	captured	0.0000254
2	captured	0.0001887
1	car	0.0008207
2	car	0.0000661
1	carbide	0.0000335
2	carbide	0.0000000
1	carbon	0.0001560
2	carbon	0.0000002
1	card	0.0000588
2	card	0.0000642
1	cardboard	0.0000243
2	cardboard	0.0000064
1	cardinal	0.0000000
2	cardinal	0.0000974
1	cards	0.0000969
2	cards	0.0000531
1	care	0.0002583
2	care	0.0004547
1	career	0.0000168
2	career	0.0002415
1	careful	0.0000130
2	careful	0.0000650
1	carefully	0.0000323
2	carefully	0.0000632
1	caretaker	0.0000000
2	caretaker	0.0000273
1	cargill	0.0000474
2	cargill	0.0000020
1	cargo	0.0001730
2	cargo	0.0000000
1	caribbean	0.0000440
2	caribbean	0.0000706
1	caring	0.0000320
2	caring	0.0000049
1	carl	0.0000549
2	carl	0.0000747
1	carla	0.0000183
2	carla	0.0000223
1	carlos	0.0000011
2	carlos	0.0001083
1	carlson	0.0000485
2	carlson	0.0000207
1	carlucci	0.0000000
2	carlucci	0.0000896
1	carol	0.0000701
2	carol	0.0000212
1	carolina	0.0001724
2	carolina	0.0001251
1	carolinas	0.0000172
2	carolinas	0.0000114
1	carolyn	0.0000023
2	carolyn	0.0000218
1	carpenter	0.0000000
2	carpenter	0.0000623
1	carpenters	0.0000000
2	carpenters	0.0000234
1	carried	0.0001876
2	carried	0.0002469
1	carrier	0.0001786
2	carrier	0.0000000
1	carriers	0.0001938
2	carriers	0.0000128
1	carries	0.0000607
2	carries	0.0000316
1	carroll	0.0000000
2	carroll	0.0000351
1	carry	0.0001855
2	carry	0.0001822
1	carrying	0.0003171
2	carrying	0.0001838
1	cars	0.0007259
2	cars	0.0000037
1	carson	0.0000000
2	carson	0.0000273
1	cart	0.0000259
2	cart	0.0000131
1	cartel	0.0000001
2	cartel	0.0000350
1	cartels	0.0000000
2	cartels	0.0000467
1	carter	0.0000207
2	carter	0.0001609
1	cartoon	0.0000391
2	cartoon	0.0000000
1	carved	0.0000335
2	carved	0.0000000
1	cascade	0.0000447
2	cascade	0.0000000
1	case	0.0001816
2	case	0.0015641
1	cases	0.0002085
2	cases	0.0004856
1	casey	0.0000082
2	casey	0.0000177
1	cash	0.0007254
2	cash	0.0000937
1	casino	0.0000555
2	casino	0.0000002
1	casinos	0.0000391
2	casinos	0.0000000
1	casper	0.0000222
2	casper	0.0000079
1	cassette	0.0000415
2	cassette	0.0000022
1	cassettes	0.0000335
2	cassettes	0.0000000
1	cast	0.0000000
2	cast	0.0001364
1	castaneda	0.0000000
2	castaneda	0.0000390
1	castillo	0.0000000
2	castillo	0.0000312
1	castle	0.0000547
2	castle	0.0000242
1	castro	0.0000004
2	castro	0.0000933
1	casual	0.0000391
2	casual	0.0000000
1	casualties	0.0000448
2	casualties	0.0000584
1	casualty	0.0000052
2	casualty	0.0000276
1	cat	0.0000669
2	cat	0.0000000
1	catastrophe	0.0000000
2	catastrophe	0.0000272
1	catastrophic	0.0000372
2	catastrophic	0.0000247
1	catch	0.0000562
2	catch	0.0000387
1	categories	0.0000620
2	categories	0.0000113
1	category	0.0000902
2	category	0.0000305
1	caterpillar	0.0000335
2	caterpillar	0.0000000
1	cathedral	0.0000019
2	cathedral	0.0000610
1	catherine	0.0000045
2	catherine	0.0000203
1	catholic	0.0000007
2	catholic	0.0003930
1	catholicjewish	0.0000000
2	catholicjewish	0.0000234
1	catholics	0.0000000
2	catholics	0.0000662
1	cattle	0.0002288
2	cattle	0.0000000
1	caucus	0.0000004
2	caucus	0.0000582
1	caucuses	0.0000000
2	caucuses	0.0000545
1	caught	0.0001625
2	caught	0.0000891
1	cause	0.0005886
2	cause	0.0001969
1	caused	0.0006235
2	caused	0.0000946
1	causes	0.0001317
2	causes	0.0000717
1	causing	0.0001463
2	causing	0.0000576
1	caution	0.0000189
2	caution	0.0000180
1	cautioned	0.0000542
2	cautioned	0.0000518
1	cautious	0.0000718
2	cautious	0.0000395
1	cautiously	0.0000000
2	cautiously	0.0000234
1	cavazos	0.0000000
2	cavazos	0.0000662
1	cave	0.0000334
2	cave	0.0000000
1	cbn	0.0000000
2	cbn	0.0000273
1	cbs	0.0004733
2	cbs	0.0000125
1	cdc	0.0001061
2	cdc	0.0000000
1	cdy	0.0001061
2	cdy	0.0000000
1	cease	0.0000000
2	cease	0.0000234
1	ceasefire	0.0000013
2	ceasefire	0.0002056
1	ceausescu	0.0000000
2	ceausescu	0.0001052
1	ceausescus	0.0000000
2	ceausescus	0.0000740
1	cebu	0.0000335
2	cebu	0.0000000
1	ceiling	0.0000557
2	ceiling	0.0000118
1	celebrate	0.0000006
2	celebrate	0.0000619
1	celebrated	0.0000140
2	celebrated	0.0000409
1	celebrating	0.0000054
2	celebrating	0.0000235
1	celebration	0.0000120
2	celebration	0.0000618
1	celebrations	0.0000000
2	celebrations	0.0000234
1	celebrities	0.0000000
2	celebrities	0.0000351
1	celebrity	0.0000000
2	celebrity	0.0000273
1	cell	0.0000533
2	cell	0.0000251
1	cells	0.0001752
2	cells	0.0000258
1	cement	0.0000370
2	cement	0.0000054
1	cemetery	0.0000007
2	cemetery	0.0001008
1	censorship	0.0000000
2	censorship	0.0000312
1	census	0.0000946
2	census	0.0000002
1	cent	0.0007758
2	cent	0.0000000
1	centennial	0.0000335
2	centennial	0.0000000
1	center	0.0009489
2	center	0.0005025
1	centered	0.0000670
2	centered	0.0000000
1	centers	0.0002062
2	centers	0.0000742
1	central	0.0009171
2	central	0.0005364
1	centrist	0.0000000
2	centrist	0.0000350
1	centrust	0.0000614
2	centrust	0.0000000
1	cents	0.0015852
2	cents	0.0000000
1	centuries	0.0000112
2	centuries	0.0000194
1	century	0.0001845
2	century	0.0001517
1	ceo	0.0000391
2	ceo	0.0000000
1	cereal	0.0000547
2	cereal	0.0000086
1	ceremonial	0.0000000
2	ceremonial	0.0000234
1	ceremonies	0.0000063
2	ceremonies	0.0000579
1	ceremony	0.0000000
2	ceremony	0.0001597
1	ceremsak	0.0000391
2	ceremsak	0.0000000
1	certainly	0.0001089
2	certainly	0.0001500
1	certainty	0.0000391
2	certainty	0.0000000
1	certificate	0.0000000
2	certificate	0.0000234
1	certificates	0.0000335
2	certificates	0.0000000
1	certification	0.0000430
2	certification	0.0000050
1	cerullo	0.0000000
2	cerullo	0.0000234
1	cesar	0.0000000
2	cesar	0.0000390
1	cftc	0.0000329
2	cftc	0.0000394
1	chadli	0.0000006
2	chadli	0.0000230
1	chafee	0.0000000
2	chafee	0.0000234
1	chain	0.0002288
2	chain	0.0000000
1	chains	0.0000558
2	chains	0.0000000
1	chair	0.0000000
2	chair	0.0000545
1	chaired	0.0000000
2	chaired	0.0000351
1	chairman	0.0008767
2	chairman	0.0007477
1	chairmen	0.0000000
2	chairmen	0.0000273
1	chairs	0.0000001
2	chairs	0.0000350
1	challenge	0.0000522
2	challenge	0.0001935
1	challenged	0.0000000
2	challenged	0.0001013
1	challenger	0.0000985
2	challenger	0.0000559
1	challengers	0.0000066
2	challengers	0.0000305
1	challenges	0.0000338
2	challenges	0.0000349
1	challenging	0.0000053
2	challenging	0.0000547
1	chamber	0.0000605
2	chamber	0.0001331
1	chambers	0.0000000
2	chambers	0.0000506
1	chamorro	0.0000000
2	chamorro	0.0001519
1	chamorros	0.0000000
2	chamorros	0.0000312
1	champagne	0.0000196
2	champagne	0.0000136
1	champion	0.0000310
2	champion	0.0001264
1	champions	0.0000000
2	champions	0.0000234
1	championship	0.0000229
2	championship	0.0000503
1	chance	0.0001619
2	chance	0.0002688
1	chancellor	0.0000054
2	chancellor	0.0001092
1	chancery	0.0000446
2	chancery	0.0000000
1	chances	0.0000658
2	chances	0.0000982
1	chaney	0.0000000
2	chaney	0.0000273
1	change	0.0002991
2	change	0.0006756
1	changed	0.0001343
2	changed	0.0001868
1	changes	0.0002481
2	changes	0.0004346
1	changing	0.0000435
2	changing	0.0000670
1	channel	0.0000726
2	channel	0.0000000
1	channels	0.0000084
2	channels	0.0000370
1	chanted	0.0000242
2	chanted	0.0000416
1	chanting	0.0000082
2	chanting	0.0000216
1	chaos	0.0000101
2	chaos	0.0000553
1	chapman	0.0000186
2	chapman	0.0000104
1	chapter	0.0000975
2	chapter	0.0000878
1	character	0.0000539
2	character	0.0000520
1	characteristics	0.0000347
2	characteristics	0.0000069
1	characterized	0.0000322
2	characterized	0.0000204
1	characters	0.0000377
2	characters	0.0000282
1	charge	0.0001513
2	charge	0.0004554
1	charged	0.0001433
2	charged	0.0006324
1	charges	0.0001454
2	charges	0.0010985
1	chargeurs	0.0000391
2	chargeurs	0.0000000
1	charging	0.0000384
2	charging	0.0000628
1	charitable	0.0000000
2	charitable	0.0000351
1	charities	0.0000030
2	charities	0.0000447
1	charity	0.0000264
2	charity	0.0000478
1	charles	0.0001775
2	charles	0.0002540
1	charleston	0.0001228
2	charleston	0.0000000
1	charlie	0.0000397
2	charlie	0.0000307
1	charlotte	0.0000256
2	charlotte	0.0000405
1	charred	0.0000502
2	charred	0.0000000
1	chart	0.0000270
2	chart	0.0000045
1	charter	0.0000554
2	charter	0.0000587
1	chartered	0.0000503
2	chartered	0.0000272
1	chase	0.0000893
2	chase	0.0000000
1	chased	0.0000217
2	chased	0.0000160
1	chatham	0.0000558
2	chatham	0.0000000
1	cheap	0.0000595
2	cheap	0.0000403
1	cheaper	0.0000837
2	cheaper	0.0000000
1	checchi	0.0000781
2	checchi	0.0000000
1	check	0.0001619
2	check	0.0000272
1	checked	0.0000584
2	checked	0.0000333
1	checking	0.0000360
2	checking	0.0000294
1	checkpoint	0.0000558
2	checkpoint	0.0000000
1	checks	0.0000658
2	checks	0.0000437
1	cheered	0.0000023
2	cheered	0.0000413
1	cheering	0.0000072
2	cheering	0.0000339
1	cheers	0.0000126
2	cheers	0.0000302
1	cheese	0.0000502
2	cheese	0.0000000
1	chelsea	0.0000000
2	chelsea	0.0000312
1	chemical	0.0003198
2	chemical	0.0000222
1	chemicals	0.0001730
2	chemicals	0.0000000
1	chemotherapy	0.0000002
2	chemotherapy	0.0000271
1	cheney	0.0000000
2	cheney	0.0001169
1	cher	0.0000000
2	cher	0.0000312
1	chernobyl	0.0000335
2	chernobyl	0.0000000
1	cherry	0.0000335
2	cherry	0.0000000
1	chess	0.0000000
2	chess	0.0000779
1	chest	0.0000716
2	chest	0.0000630
1	chester	0.0000029
2	chester	0.0000642
1	cheyenne	0.0000321
2	cheyenne	0.0000127
1	chia	0.0000335
2	chia	0.0000000
1	chicago	0.0006025
2	chicago	0.0001444
1	chicagobased	0.0000837
2	chicagobased	0.0000000
1	chicken	0.0000413
2	chicken	0.0000101
1	chickens	0.0000669
2	chickens	0.0000000
1	chief	0.0010781
2	chief	0.0008955
1	chiefs	0.0000088
2	chiefs	0.0000601
1	child	0.0001388
2	child	0.0003473
1	childhood	0.0000346
2	childhood	0.0000460
1	children	0.0007105
2	children	0.0008676
1	childrens	0.0001358
2	childrens	0.0000533
1	childs	0.0000378
2	childs	0.0000048
1	chile	0.0000081
2	chile	0.0000255
1	chiles	0.0000000
2	chiles	0.0000234
1	china	0.0001017
2	china	0.0004043
1	chinas	0.0000000
2	chinas	0.0001208
1	chinese	0.0000898
2	chinese	0.0002490
1	chinn	0.0000837
2	chinn	0.0000000
1	chip	0.0000713
2	chip	0.0000087
1	chips	0.0001898
2	chips	0.0000000
1	chiquita	0.0000447
2	chiquita	0.0000000
1	choice	0.0000654
2	choice	0.0002387
1	choices	0.0000000
2	choices	0.0000468
1	choir	0.0000000
2	choir	0.0000390
1	cholesterol	0.0000196
2	cholesterol	0.0000097
1	choose	0.0000619
2	choose	0.0000776
1	choosing	0.0000395
2	choosing	0.0000309
1	chorus	0.0000000
2	chorus	0.0000273
1	chose	0.0000000
2	chose	0.0000974
1	chosen	0.0000150
2	chosen	0.0001648
1	chris	0.0000788
2	chris	0.0000268
1	christ	0.0000000
2	christ	0.0001208
1	christian	0.0000187
2	christian	0.0003259
1	christians	0.0000002
2	christians	0.0000739
1	christies	0.0000781
2	christies	0.0000000
1	christine	0.0000179
2	christine	0.0000109
1	christmas	0.0001894
2	christmas	0.0001288
1	christopher	0.0000120
2	christopher	0.0000656
1	chromium	0.0000335
2	chromium	0.0000000
1	chronic	0.0000426
2	chronic	0.0000287
1	chrysler	0.0003628
2	chrysler	0.0000000
1	chryslers	0.0000614
2	chryslers	0.0000000
1	chuck	0.0000423
2	chuck	0.0000094
1	church	0.0000129
2	church	0.0006611
1	churches	0.0000000
2	churches	0.0000662
1	churchs	0.0000000
2	churchs	0.0000312
1	cia	0.0000000
2	cia	0.0001091
1	cigarette	0.0000760
2	cigarette	0.0000210
1	cigarettes	0.0001837
2	cigarettes	0.0000003
1	cincinnati	0.0000500
2	cincinnati	0.0000391
1	cinema	0.0000277
2	cinema	0.0000040
1	circle	0.0000000
2	circle	0.0000234
1	circles	0.0000548
2	circles	0.0000241
1	circuit	0.0000475
2	circuit	0.0001772
1	circular	0.0000335
2	circular	0.0000000
1	circulated	0.0000000
2	circulated	0.0000234
1	circulation	0.0000297
2	circulation	0.0000338
1	circumstances	0.0000149
2	circumstances	0.0000948
1	circus	0.0000721
2	circus	0.0000081
1	cisneros	0.0000148
2	cisneros	0.0000364
1	cited	0.0001840
2	cited	0.0001520
1	cites	0.0000345
2	cites	0.0000032
1	cities	0.0003555
2	cities	0.0002428
1	citing	0.0000185
2	citing	0.0001040
1	citizen	0.0000382
2	citizen	0.0001058
1	citizens	0.0001053
2	citizens	0.0003511
1	citizenship	0.0000000
2	citizenship	0.0000468
1	city	0.0016659
2	city	0.0012761
1	citys	0.0002195
2	citys	0.0001585
1	civic	0.0000190
2	civic	0.0000374
1	civil	0.0000522
2	civil	0.0005908
1	civilian	0.0001579
2	civilian	0.0001820
1	civilians	0.0000051
2	civilians	0.0001835
1	civilization	0.0000164
2	civilization	0.0000236
1	claim	0.0000924
2	claim	0.0003562
1	claimants	0.0000000
2	claimants	0.0000312
1	claimed	0.0000934
2	claimed	0.0004257
1	claiming	0.0000260
2	claiming	0.0000948
1	claims	0.0001981
2	claims	0.0003994
1	clandestine	0.0000039
2	clandestine	0.0000284
1	clara	0.0000613
2	clara	0.0000079
1	claremont	0.0000389
2	claremont	0.0000001
1	clark	0.0000325
2	clark	0.0001059
1	clarke	0.0000081
2	clarke	0.0000294
1	clash	0.0000322
2	clash	0.0000399
1	clashed	0.0000082
2	clashed	0.0000527
1	clashes	0.0000027
2	clashes	0.0001111
1	class	0.0000890
2	class	0.0002067
1	classaction	0.0000000
2	classaction	0.0000351
1	classes	0.0000320
2	classes	0.0001180
1	classic	0.0000444
2	classic	0.0000080
1	classics	0.0000391
2	classics	0.0000000
1	classified	0.0000237
2	classified	0.0001159
1	classroom	0.0000000
2	classroom	0.0000896
1	claude	0.0000224
2	claude	0.0000194
1	clay	0.0000168
2	clay	0.0000194
1	clayton	0.0000156
2	clayton	0.0000358
1	clean	0.0001558
2	clean	0.0000627
1	cleaned	0.0000333
2	cleaned	0.0000001
1	cleaning	0.0000835
2	cleaning	0.0000001
1	cleanup	0.0001488
2	cleanup	0.0000013
1	clear	0.0001531
2	clear	0.0004619
1	cleared	0.0000889
2	cleared	0.0000470
1	clearing	0.0000334
2	clearing	0.0000079
1	clearly	0.0000376
2	clearly	0.0002231
1	clears	0.0000000
2	clears	0.0000234
1	clem	0.0000000
2	clem	0.0000234
1	clerical	0.0000269
2	clerical	0.0000046
1	clerk	0.0000330
2	clerk	0.0000237
1	clerks	0.0000052
2	clerks	0.0000353
1	cleveland	0.0000816
2	cleveland	0.0000482
1	client	0.0000004
2	client	0.0000893
1	clients	0.0000405
2	clients	0.0000574
1	cliff	0.0000224
2	cliff	0.0000156
1	climate	0.0000394
2	climate	0.0000426
1	climb	0.0000312
2	climb	0.0000133
1	climbed	0.0002790
2	climbed	0.0000000
1	climbing	0.0000614
2	climbing	0.0000000
1	clinic	0.0000336
2	clinic	0.0000194
1	clinics	0.0000111
2	clinics	0.0000546
1	clint	0.0000000
2	clint	0.0000234
1	clinton	0.0000000
2	clinton	0.0000312
1	clock	0.0000346
2	clock	0.0000265
1	close	0.0010342
2	close	0.0003768
1	closed	0.0008187
2	closed	0.0001610
1	closely	0.0001158
2	closely	0.0000477
1	closer	0.0000494
2	closer	0.0000863
1	closes	0.0000335
2	closes	0.0000000
1	closest	0.0000572
2	closest	0.0000380
1	closing	0.0003218
2	closing	0.0000910
1	closings	0.0000447
2	closings	0.0000000
1	cloth	0.0000318
2	cloth	0.0000012
1	clothes	0.0001170
2	clothes	0.0000275
1	clothing	0.0001722
2	clothing	0.0000044
1	cloud	0.0000272
2	cloud	0.0000122
1	clouds	0.0000833
2	clouds	0.0000003
1	cloudy	0.0001451
2	cloudy	0.0000000
1	clout	0.0000000
2	clout	0.0000273
1	clr	0.0000949
2	clr	0.0000000
1	club	0.0000300
2	club	0.0003414
1	clubs	0.0000000
2	clubs	0.0001013
1	clues	0.0000502
2	clues	0.0000000
1	clyde	0.0000002
2	clyde	0.0000233
1	cmdr	0.0000502
2	cmdr	0.0000000
1	cna	0.0000391
2	cna	0.0000000
1	cnn	0.0000000
2	cnn	0.0000974
1	co	0.0015821
2	co	0.0000138
1	coach	0.0000128
2	coach	0.0000417
1	coal	0.0001052
2	coal	0.0000746
1	coalition	0.0000252
2	coalition	0.0003213
1	coast	0.0006800
2	coast	0.0000552
1	coastal	0.0001332
2	coastal	0.0000005
1	coastamerica	0.0000391
2	coastamerica	0.0000000
1	coasters	0.0000335
2	coasters	0.0000000
1	coasts	0.0000151
2	coasts	0.0000129
1	coat	0.0000502
2	coat	0.0000000
1	coats	0.0000000
2	coats	0.0000390
1	coca	0.0000116
2	coca	0.0000269
1	cocaine	0.0000455
2	cocaine	0.0003968
1	cochairman	0.0000135
2	cochairman	0.0000217
1	cockpit	0.0000609
2	cockpit	0.0000004
1	cocoa	0.0000391
2	cocoa	0.0000000
1	code	0.0000332
2	code	0.0000625
1	coffee	0.0000634
2	coffee	0.0000064
1	coffers	0.0000128
2	coffers	0.0000183
1	coffin	0.0000077
2	coffin	0.0000258
1	cohost	0.0000007
2	cohost	0.0000229
1	coins	0.0000177
2	coins	0.0000110
1	col	0.0000975
2	col	0.0001151
1	cold	0.0002096
2	cold	0.0000835
1	cole	0.0000646
2	cole	0.0000251
1	coleco	0.0000726
2	coleco	0.0000000
1	coleman	0.0000166
2	coleman	0.0000273
1	colin	0.0000000
2	colin	0.0000273
1	collaborating	0.0000020
2	collaborating	0.0000220
1	collapse	0.0000951
2	collapse	0.0000466
1	collapsed	0.0001136
2	collapsed	0.0000570
1	collapsing	0.0000152
2	collapsing	0.0000244
1	collateral	0.0000447
2	collateral	0.0000000
1	colleague	0.0000132
2	colleague	0.0000180
1	colleagues	0.0000400
2	colleagues	0.0001357
1	collect	0.0000455
2	collect	0.0000306
1	collected	0.0001115
2	collected	0.0000547
1	collecting	0.0000465
2	collecting	0.0000065
1	collection	0.0000725
2	collection	0.0001247
1	collections	0.0000436
2	collections	0.0000202
1	collective	0.0000409
2	collective	0.0000533
1	collector	0.0000309
2	collector	0.0000057
1	collectors	0.0000614
2	collectors	0.0000000
1	college	0.0000880
2	college	0.0004489
1	colleges	0.0000140
2	colleges	0.0000409
1	collided	0.0000447
2	collided	0.0000000
1	collider	0.0000374
2	collider	0.0000011
1	collins	0.0000236
2	collins	0.0000108
1	collision	0.0000558
2	collision	0.0000000
1	colo	0.0000920
2	colo	0.0000137
1	colombia	0.0000003
2	colombia	0.0000972
1	colombian	0.0000000
2	colombian	0.0000623
1	colombias	0.0000052
2	colombias	0.0000314
1	colombo	0.0000335
2	colombo	0.0000000
1	colonel	0.0000116
2	colonel	0.0000348
1	colonial	0.0000003
2	colonial	0.0000348
1	colonies	0.0000162
2	colonies	0.0000199
1	colony	0.0000497
2	colony	0.0000666
1	color	0.0000415
2	color	0.0000802
1	colorado	0.0001792
2	colorado	0.0000425
1	colored	0.0000082
2	colored	0.0000177
1	colorful	0.0000019
2	colorful	0.0000220
1	colors	0.0000157
2	colors	0.0000163
1	columbia	0.0001931
2	columbia	0.0001340
1	columbias	0.0000502
2	columbias	0.0000000
1	columbus	0.0000632
2	columbus	0.0000221
1	column	0.0000000
2	column	0.0000662
1	columnist	0.0000016
2	columnist	0.0000885
1	coma	0.0000014
2	coma	0.0000224
1	combat	0.0000913
2	combat	0.0001778
1	combatants	0.0000003
2	combatants	0.0000232
1	combe	0.0000000
2	combe	0.0000390
1	combination	0.0000541
2	combination	0.0000206
1	combine	0.0000335
2	combine	0.0000000
1	combined	0.0001575
2	combined	0.0000342
1	come	0.0005590
2	come	0.0007319
1	comedy	0.0000549
2	comedy	0.0000318
1	comes	0.0001498
2	comes	0.0002110
1	comfort	0.0000039
2	comfort	0.0000206
1	comfortable	0.0000365
2	comfortable	0.0000486
1	coming	0.0002436
2	coming	0.0002702
1	command	0.0001041
2	command	0.0001689
1	commandant	0.0000025
2	commandant	0.0000255
1	commander	0.0001341
2	commander	0.0001596
1	commanders	0.0000077
2	commanders	0.0000452
1	commanding	0.0000137
2	commanding	0.0000177
1	commencement	0.0000000
2	commencement	0.0000234
1	comment	0.0002640
2	comment	0.0004469
1	commentary	0.0000000
2	commentary	0.0000351
1	commentator	0.0000000
2	commentator	0.0000234
1	commented	0.0000100
2	commented	0.0000709
1	commenting	0.0000070
2	commenting	0.0000263
1	comments	0.0000000
2	comments	0.0002532
1	commerce	0.0003921
2	commerce	0.0000692
1	commercial	0.0004589
2	commercial	0.0000654
1	commerciale	0.0000335
2	commerciale	0.0000000
1	commercials	0.0000001
2	commercials	0.0000857
1	commission	0.0002542
2	commission	0.0005122
1	commissioned	0.0000267
2	commissioned	0.0000087
1	commissioner	0.0000559
2	commissioner	0.0001363
1	commissions	0.0000033
2	commissions	0.0000328
1	commit	0.0000255
2	commit	0.0000757
1	commitment	0.0000651
2	commitment	0.0001221
1	commitments	0.0000000
2	commitments	0.0000545
1	committed	0.0000154
2	committed	0.0002113
1	committee	0.0001185
2	committee	0.0018380
1	committees	0.0000000
2	committees	0.0002493
1	committing	0.0000000
2	committing	0.0000584
1	commodities	0.0001276
2	commodities	0.0000083
1	commodity	0.0001730
2	commodity	0.0000000
1	common	0.0004524
2	common	0.0001673
1	commonly	0.0000509
2	commonly	0.0000113
1	commons	0.0000067
2	commons	0.0000537
1	commune	0.0000447
2	commune	0.0000000
1	communicate	0.0000029
2	communicate	0.0000331
1	communication	0.0000000
2	communication	0.0000545
1	communications	0.0002514
2	communications	0.0000427
1	communion	0.0000000
2	communion	0.0000273
1	communique	0.0000391
2	communique	0.0000740
1	communism	0.0000000
2	communism	0.0000545
1	communist	0.0000000
2	communist	0.0009818
1	communists	0.0000000
2	communists	0.0001948
1	communities	0.0000960
2	communities	0.0000966
1	community	0.0002018
2	community	0.0007085
1	communitys	0.0000007
2	communitys	0.0000345
1	commuter	0.0000558
2	commuter	0.0000000
1	compact	0.0000308
2	compact	0.0000136
1	companies	0.0012034
2	companies	0.0000600
1	companion	0.0000345
2	companion	0.0000149
1	company	0.0034833
2	company	0.0000581
1	companys	0.0006745
2	companys	0.0000006
1	comparable	0.0001324
2	comparable	0.0000050
1	compare	0.0000153
2	compare	0.0000205
1	compared	0.0008162
2	compared	0.0000303
1	compares	0.0000387
2	compares	0.0000003
1	comparison	0.0000845
2	comparison	0.0000034
1	compelled	0.0000006
2	compelled	0.0000308
1	compensate	0.0000590
2	compensate	0.0000329
1	compensation	0.0000991
2	compensation	0.0000516
1	compete	0.0000457
2	compete	0.0000383
1	competent	0.0000000
2	competent	0.0000390
1	competing	0.0000577
2	competing	0.0000221
1	competition	0.0001545
2	competition	0.0000909
1	competitive	0.0001658
2	competitive	0.0000050
1	competitor	0.0000289
2	competitor	0.0000071
1	competitors	0.0000949
2	competitors	0.0000000
1	compiled	0.0000447
2	compiled	0.0000000
1	complain	0.0000264
2	complain	0.0000322
1	complained	0.0000470
2	complained	0.0002166
1	complaining	0.0000239
2	complaining	0.0000535
1	complaint	0.0000735
2	complaint	0.0001084
1	complaints	0.0000667
2	complaints	0.0000898
1	complete	0.0001283
2	complete	0.0001598
1	completed	0.0001950
2	completed	0.0001366
1	completely	0.0000162
2	completely	0.0000978
1	completing	0.0000290
2	completing	0.0000070
1	completion	0.0000515
2	completion	0.0000186
1	complex	0.0002509
2	complex	0.0001132
1	compliance	0.0000734
2	compliance	0.0000306
1	complicated	0.0000026
2	complicated	0.0000800
1	complications	0.0000324
2	complications	0.0000008
1	complicity	0.0000000
2	complicity	0.0000273
1	comply	0.0000052
2	comply	0.0000743
1	complying	0.0000140
2	complying	0.0000175
1	component	0.0000614
2	component	0.0000000
1	components	0.0000667
2	components	0.0000041
1	composed	0.0000004
2	composed	0.0000309
1	composer	0.0000007
2	composer	0.0000229
1	composite	0.0003516
2	composite	0.0000000
1	compound	0.0000712
2	compound	0.0000126
1	comprehensive	0.0000264
2	comprehensive	0.0000595
1	comprise	0.0000394
2	comprise	0.0000154
1	comprised	0.0000236
2	comprised	0.0000147
1	compromise	0.0000000
2	compromise	0.0002221
1	compromises	0.0000000
2	compromises	0.0000273
1	computer	0.0009180
2	computer	0.0000099
1	computerized	0.0000670
2	computerized	0.0000000
1	computers	0.0001874
2	computers	0.0000055
1	concede	0.0000103
2	concede	0.0000318
1	conceded	0.0000000
2	conceded	0.0000740
1	conceicao	0.0000391
2	conceicao	0.0000000
1	concentrate	0.0000338
2	concentrate	0.0000232
1	concentrated	0.0000590
2	concentrated	0.0000017
1	concentrating	0.0000056
2	concentrating	0.0000234
1	concentration	0.0000349
2	concentration	0.0000068
1	concept	0.0000000
2	concept	0.0000506
1	concern	0.0002807
2	concern	0.0002248
1	concerned	0.0001590
2	concerned	0.0002357
1	concerning	0.0000000
2	concerning	0.0000818
1	concerns	0.0002731
2	concerns	0.0001484
1	concert	0.0000220
2	concert	0.0001054
1	concerts	0.0000389
2	concerts	0.0000001
1	concession	0.0000185
2	concession	0.0000222
1	concessions	0.0000314
2	concessions	0.0000911
1	conciliatory	0.0000000
2	conciliatory	0.0000351
1	conclude	0.0000020
2	conclude	0.0000493
1	concluded	0.0000956
2	concluded	0.0001008
1	concludes	0.0000120
2	concludes	0.0000189
1	conclusion	0.0000915
2	conclusion	0.0000452
1	conclusions	0.0000216
2	conclusions	0.0000316
1	concrete	0.0001133
2	concrete	0.0000378
1	condemn	0.0000001
2	condemn	0.0000272
1	condemnation	0.0000000
2	condemnation	0.0000312
1	condemned	0.0000016
2	condemned	0.0001157
1	condemning	0.0000000
2	condemning	0.0000234
1	condition	0.0005868
2	condition	0.0005566
1	conditioned	0.0000213
2	conditioned	0.0000124
1	conditions	0.0004278
2	conditions	0.0002040
1	condom	0.0000558
2	condom	0.0000000
1	condoms	0.0001228
2	condoms	0.0000000
1	conduct	0.0000439
2	conduct	0.0002109
1	conducted	0.0001248
2	conducted	0.0003025
1	conducting	0.0000564
2	conducting	0.0000541
1	conductor	0.0000000
2	conductor	0.0000351
1	confederation	0.0000164
2	confederation	0.0000119
1	conference	0.0000956
2	conference	0.0009969
1	conferences	0.0000000
2	conferences	0.0000584
1	confessed	0.0000007
2	confessed	0.0000346
1	confession	0.0000000
2	confession	0.0000234
1	confidence	0.0001153
2	confidence	0.0000948
1	confident	0.0000000
2	confident	0.0001052
1	confidential	0.0000000
2	confidential	0.0000545
1	confidentiality	0.0000000
2	confidentiality	0.0000312
1	confined	0.0000128
2	confined	0.0000300
1	confinement	0.0000000
2	confinement	0.0000273
1	confirm	0.0000397
2	confirm	0.0000735
1	confirmation	0.0000312
2	confirmation	0.0000406
1	confirmed	0.0001504
2	confirmed	0.0001171
1	confirms	0.0000193
2	confirms	0.0000099
1	confiscated	0.0000444
2	confiscated	0.0000508
1	conflict	0.0000448
2	conflict	0.0001830
1	conflicting	0.0000228
2	conflicting	0.0000192
1	conflicts	0.0000000
2	conflicts	0.0000428
1	confrontation	0.0000075
2	confrontation	0.0000649
1	confusion	0.0000374
2	confusion	0.0000518
1	conglomerate	0.0000501
2	conglomerate	0.0000079
1	congress	0.0000755
2	congress	0.0018096
1	congressional	0.0000359
2	congressional	0.0005126
1	congressman	0.0000000
2	congressman	0.0000818
1	congressmen	0.0000000
2	congressmen	0.0000390
1	coniston	0.0000391
2	coniston	0.0000000
1	conn	0.0001266
2	conn	0.0000246
1	connect	0.0000335
2	connect	0.0000000
1	connected	0.0000005
2	connected	0.0000464
1	connecticut	0.0000555
2	connecticut	0.0000664
1	connection	0.0001170
2	connection	0.0001404
1	connections	0.0000179
2	connections	0.0000304
1	connie	0.0000165
2	connie	0.0000119
1	conrad	0.0000891
2	conrad	0.0000001
1	conrail	0.0000558
2	conrail	0.0000000
1	conscience	0.0000000
2	conscience	0.0000506
1	conscious	0.0000114
2	conscious	0.0000232
1	consecutive	0.0001408
2	consecutive	0.0000147
1	consensus	0.0000165
2	consensus	0.0000586
1	consent	0.0000219
2	consent	0.0000821
1	consequences	0.0000098
2	consequences	0.0000516
1	conservation	0.0000891
2	conservation	0.0000001
1	conservative	0.0000417
2	conservative	0.0005046
1	conservatives	0.0000000
2	conservatives	0.0001909
1	consider	0.0000889
2	consider	0.0003276
1	considerable	0.0000445
2	considerable	0.0000547
1	considerably	0.0000309
2	considerably	0.0000096
1	consideration	0.0000282
2	consideration	0.0001050
1	considerations	0.0000123
2	considerations	0.0000264
1	considered	0.0002556
2	considered	0.0003748
1	considering	0.0001570
2	considering	0.0001631
1	considers	0.0000161
2	considers	0.0000316
1	consist	0.0000122
2	consist	0.0000149
1	consistent	0.0000395
2	consistent	0.0000387
1	consistently	0.0000059
2	consistently	0.0000387
1	consists	0.0000344
2	consists	0.0000189
1	consolidated	0.0001284
2	consolidated	0.0000000
1	consortium	0.0000455
2	consortium	0.0000033
1	conspiracy	0.0000006
2	conspiracy	0.0002217
1	conspiring	0.0000000
2	conspiring	0.0000351
1	constable	0.0000207
2	constable	0.0000089
1	constabulary	0.0000447
2	constabulary	0.0000000
1	constant	0.0000075
2	constant	0.0000259
1	constantly	0.0000166
2	constantly	0.0000312
1	constituents	0.0000000
2	constituents	0.0000429
1	constitution	0.0000000
2	constitution	0.0003312
1	constitutional	0.0000000
2	constitutional	0.0002299
1	constitutionally	0.0000000
2	constitutionally	0.0000234
1	constitutions	0.0000000
2	constitutions	0.0000234
1	construction	0.0003473
2	construction	0.0001004
1	constructive	0.0000109
2	constructive	0.0000196
1	consul	0.0000006
2	consul	0.0000230
1	consular	0.0000003
2	consular	0.0000310
1	consulate	0.0000793
2	consulate	0.0000031
1	consultant	0.0000792
2	consultant	0.0000889
1	consultants	0.0000134
2	consultants	0.0000647
1	consultation	0.0000000
2	consultation	0.0000234
1	consultations	0.0000052
2	consultations	0.0000197
1	consulted	0.0000166
2	consulted	0.0000235
1	consulting	0.0000731
2	consulting	0.0000230
1	consumed	0.0000513
2	consumed	0.0000071
1	consumer	0.0005245
2	consumer	0.0000001
1	consumers	0.0002978
2	consumers	0.0000025
1	consumption	0.0000850
2	consumption	0.0000069
1	contact	0.0001916
2	contact	0.0001663
1	contacted	0.0000399
2	contacted	0.0000384
1	contacts	0.0000044
2	contacts	0.0001177
1	contagious	0.0000335
2	contagious	0.0000000
1	contain	0.0000545
2	contain	0.0000360
1	contained	0.0000906
2	contained	0.0001082
1	container	0.0000502
2	container	0.0000000
1	containers	0.0000335
2	containers	0.0000000
1	containing	0.0000749
2	containing	0.0000529
1	contains	0.0000581
2	contains	0.0000530
1	contaminated	0.0000670
2	contaminated	0.0000000
1	contamination	0.0000391
2	contamination	0.0000000
1	contemporary	0.0000037
2	contemporary	0.0000247
1	contempt	0.0000000
2	contempt	0.0000701
1	contend	0.0000398
2	contend	0.0000813
1	contended	0.0000146
2	contended	0.0001456
1	contender	0.0000000
2	contender	0.0000273
1	contenders	0.0000000
2	contenders	0.0000351
1	contending	0.0000011
2	contending	0.0000421
1	contends	0.0000347
2	contends	0.0000965
1	content	0.0000022
2	content	0.0000491
1	contention	0.0000002
2	contention	0.0000271
1	contents	0.0000319
2	contents	0.0000167
1	contest	0.0000420
2	contest	0.0000915
1	contestants	0.0000558
2	contestants	0.0000000
1	contested	0.0000074
2	contested	0.0000260
1	contests	0.0000000
2	contests	0.0000234
1	context	0.0000000
2	context	0.0000273
1	continent	0.0000000
2	continent	0.0000351
1	continental	0.0002335
2	continental	0.0000162
1	continentals	0.0000502
2	continentals	0.0000000
1	contingent	0.0000483
2	contingent	0.0000091
1	continue	0.0004789
2	continue	0.0004644
1	continued	0.0005818
2	continued	0.0002523
1	continues	0.0001054
2	continues	0.0001213
1	continuing	0.0001987
2	continuing	0.0001730
1	contra	0.0000066
2	contra	0.0002876
1	contraception	0.0000000
2	contraception	0.0000234
1	contract	0.0010272
2	contract	0.0000389
1	contracted	0.0000375
2	contracted	0.0000206
1	contracting	0.0000430
2	contracting	0.0000167
1	contractor	0.0001946
2	contractor	0.0000005
1	contractors	0.0001310
2	contractors	0.0000566
1	contracts	0.0004793
2	contracts	0.0000589
1	contrary	0.0000000
2	contrary	0.0000584
1	contras	0.0000000
2	contras	0.0002883
1	contrast	0.0000429
2	contrast	0.0000714
1	contribute	0.0000402
2	contribute	0.0000693
1	contributed	0.0000946
2	contributed	0.0000703
1	contributing	0.0000344
2	contributing	0.0000266
1	contribution	0.0000179
2	contribution	0.0000576
1	contributions	0.0000206
2	contributions	0.0002232
1	contributors	0.0000000
2	contributors	0.0000234
1	control	0.0005667
2	control	0.0008395
1	controlled	0.0000277
2	controlled	0.0001326
1	controllers	0.0001116
2	controllers	0.0000000
1	controlling	0.0000732
2	controlling	0.0000073
1	controls	0.0002462
2	controls	0.0001008
1	controversial	0.0000000
2	controversial	0.0000584
1	controversy	0.0000136
2	controversy	0.0001074
1	convenience	0.0000082
2	convenience	0.0000177
1	convention	0.0000001
2	convention	0.0005805
1	conventional	0.0000238
2	conventional	0.0000847
1	conventions	0.0000000
2	conventions	0.0000390
1	conversation	0.0000000
2	conversation	0.0001052
1	conversations	0.0000000
2	conversations	0.0000623
1	conversion	0.0000596
2	conversion	0.0000012
1	converted	0.0000316
2	converted	0.0000247
1	converting	0.0000170
2	converting	0.0000115
1	convict	0.0000000
2	convict	0.0000234
1	convicted	0.0000000
2	convicted	0.0006156
1	conviction	0.0000084
2	conviction	0.0001967
1	convictions	0.0000034
2	convictions	0.0000678
1	convince	0.0000054
2	convince	0.0000430
1	convinced	0.0000578
2	convinced	0.0001194
1	convoy	0.0001563
2	convoy	0.0000000
1	convoys	0.0000446
2	convoys	0.0000000
1	cook	0.0002253
2	cook	0.0000141
1	cookies	0.0000558
2	cookies	0.0000000
1	cooking	0.0000781
2	cooking	0.0000000
1	cooks	0.0000335
2	cooks	0.0000000
1	cool	0.0000879
2	cool	0.0000010
1	cooler	0.0000726
2	cooler	0.0000000
1	cooper	0.0000267
2	cooper	0.0000242
1	cooperate	0.0000000
2	cooperate	0.0001013
1	cooperated	0.0000000
2	cooperated	0.0000234
1	cooperating	0.0000231
2	cooperating	0.0000462
1	cooperation	0.0000363
2	cooperation	0.0002786
1	cooperative	0.0000474
2	cooperative	0.0000254
1	coordinate	0.0000176
2	coordinate	0.0000267
1	coordinated	0.0000127
2	coordinated	0.0000457
1	coordinator	0.0000358
2	coordinator	0.0000217
1	coowner	0.0000335
2	coowner	0.0000000
1	cop	0.0000076
2	cop	0.0000220
1	cope	0.0000134
2	cope	0.0000413
1	copies	0.0000470
2	copies	0.0001113
1	copper	0.0000335
2	copper	0.0000000
1	copy	0.0000422
2	copy	0.0000718
1	copyright	0.0000064
2	copyright	0.0000579
1	coral	0.0000529
2	coral	0.0000020
1	corazon	0.0000047
2	corazon	0.0000513
1	corbin	0.0000000
2	corbin	0.0000273
1	cord	0.0000080
2	cord	0.0000411
1	cordero	0.0000085
2	cordero	0.0000252
1	cordon	0.0000255
2	cordon	0.0000056
1	core	0.0000704
2	core	0.0000171
1	corkscrew	0.0000391
2	corkscrew	0.0000000
1	corn	0.0005079
2	corn	0.0000000
1	corner	0.0000199
2	corner	0.0000251
1	corners	0.0000335
2	corners	0.0000000
1	coroner	0.0000614
2	coroner	0.0000000
1	coroners	0.0000558
2	coroners	0.0000000
1	corp	0.0015849
2	corp	0.0000236
1	corporate	0.0004974
2	corporate	0.0000229
1	corporation	0.0001326
2	corporation	0.0000243
1	corporations	0.0001989
2	corporations	0.0000248
1	corps	0.0002228
2	corps	0.0000977
1	correct	0.0000998
2	correct	0.0000628
1	corrected	0.0000433
2	corrected	0.0000127
1	correction	0.0000391
2	correction	0.0000000
1	correctional	0.0000149
2	correctional	0.0000325
1	corrections	0.0000000
2	corrections	0.0000623
1	correctly	0.0000090
2	correctly	0.0000210
1	correspondent	0.0000000
2	correspondent	0.0001013
1	correspondents	0.0000000
2	correspondents	0.0000312
1	corroon	0.0000000
2	corroon	0.0000584
1	corrupt	0.0000000
2	corrupt	0.0000428
1	corruption	0.0000000
2	corruption	0.0001130
1	cos	0.0001116
2	cos	0.0000000
1	cosby	0.0000458
2	cosby	0.0000031
1	cosell	0.0000558
2	cosell	0.0000000
1	cosmetics	0.0000543
2	cosmetics	0.0000127
1	cost	0.0009312
2	cost	0.0002110
1	costa	0.0000697
2	costa	0.0000721
1	costing	0.0000256
2	costing	0.0000172
1	costly	0.0000663
2	costly	0.0000395
1	costs	0.0006958
2	costs	0.0000247
1	costume	0.0000000
2	costume	0.0000390
1	cotton	0.0000670
2	cotton	0.0000000
1	couldnt	0.0001060
2	couldnt	0.0001676
1	council	0.0001597
2	council	0.0007534
1	councilman	0.0000136
2	councilman	0.0000178
1	councils	0.0000000
2	councils	0.0000701
1	counsel	0.0000105
2	counsel	0.0001680
1	counseling	0.0000000
2	counseling	0.0000623
1	counselor	0.0000009
2	counselor	0.0000578
1	counselors	0.0000000
2	counselors	0.0000234
1	count	0.0002010
2	count	0.0001636
1	countdown	0.0000502
2	countdown	0.0000000
1	counted	0.0000202
2	counted	0.0000366
1	counter	0.0000779
2	counter	0.0000469
1	countered	0.0000001
2	countered	0.0000389
1	counterpart	0.0000276
2	counterpart	0.0000352
1	counterparts	0.0000139
2	counterparts	0.0000215
1	counties	0.0001109
2	counties	0.0000005
1	counting	0.0000554
2	counting	0.0000354
1	countries	0.0003640
2	countries	0.0009225
1	country	0.0004798
2	country	0.0015312
1	countrys	0.0000882
2	countrys	0.0003826
1	countryside	0.0000199
2	countryside	0.0000212
1	counts	0.0000157
2	counts	0.0002384
1	county	0.0007338
2	county	0.0004618
1	countys	0.0000213
2	countys	0.0000124
1	coup	0.0000000
2	coup	0.0002299
1	couple	0.0001469
2	couple	0.0001429
1	coupled	0.0000254
2	coupled	0.0000251
1	couples	0.0000737
2	couples	0.0000810
1	courage	0.0000000
2	courage	0.0000273
1	course	0.0002024
2	course	0.0002444
1	courses	0.0000102
2	courses	0.0000630
1	court	0.0000548
2	court	0.0026928
1	courtappointed	0.0000000
2	courtappointed	0.0000429
1	courthouse	0.0000000
2	courthouse	0.0000506
1	courting	0.0000177
2	courting	0.0000149
1	courtmartial	0.0000000
2	courtmartial	0.0000545
1	courtroom	0.0000000
2	courtroom	0.0000818
1	courts	0.0000000
2	courts	0.0003156
1	cousin	0.0000867
2	cousin	0.0000018
1	cousins	0.0000238
2	cousins	0.0000068
1	couture	0.0000000
2	couture	0.0000390
1	cove	0.0000328
2	cove	0.0000044
1	cover	0.0001539
2	cover	0.0001419
1	coverage	0.0001914
2	coverage	0.0000261
1	covered	0.0001288
2	covered	0.0001205
1	covering	0.0000682
2	covering	0.0000381
1	covers	0.0000586
2	covers	0.0000176
1	covert	0.0000000
2	covert	0.0000351
1	cowboy	0.0000282
2	cowboy	0.0000037
1	cows	0.0001099
2	cows	0.0000012
1	cox	0.0000510
2	cox	0.0000189
1	crack	0.0000458
2	crack	0.0000732
1	crackdown	0.0000000
2	crackdown	0.0001130
1	cracked	0.0000307
2	cracked	0.0000098
1	cracks	0.0000360
2	cracks	0.0000138
1	craft	0.0001336
2	craft	0.0000431
1	craig	0.0000467
2	craig	0.0000609
1	crandall	0.0000335
2	crandall	0.0000000
1	cranston	0.0000000
2	cranston	0.0000584
1	crash	0.0004678
2	crash	0.0000124
1	crashed	0.0002177
2	crashed	0.0000000
1	crater	0.0000391
2	crater	0.0000000
1	crawford	0.0000614
2	crawford	0.0000000
1	crazy	0.0000367
2	crazy	0.0000406
1	cream	0.0000301
2	cream	0.0000297
1	create	0.0001269
2	create	0.0001958
1	created	0.0000879
2	created	0.0002854
1	creates	0.0000142
2	creates	0.0000213
1	creating	0.0000397
2	creating	0.0000892
1	creation	0.0000400
2	creation	0.0000422
1	creative	0.0000000
2	creative	0.0000312
1	credentials	0.0000002
2	credentials	0.0000232
1	credibility	0.0000000
2	credibility	0.0000506
1	credible	0.0000058
2	credible	0.0000310
1	credit	0.0004573
2	credit	0.0000470
1	credited	0.0000198
2	credited	0.0000446
1	creditor	0.0000324
2	creditor	0.0000047
1	creditors	0.0002270
2	creditors	0.0000052
1	credits	0.0000216
2	credits	0.0000356
1	creek	0.0002400
2	creek	0.0000000
1	creque	0.0000391
2	creque	0.0000000
1	crew	0.0004577
2	crew	0.0000000
1	crewmen	0.0000607
2	crewmen	0.0000044
1	crews	0.0001619
2	crews	0.0000000
1	cried	0.0000009
2	cried	0.0000305
1	crime	0.0000140
2	crime	0.0004422
1	crimes	0.0000000
2	crimes	0.0003117
1	criminal	0.0000007
2	criminal	0.0004904
1	criminals	0.0000000
2	criminals	0.0000545
1	crippled	0.0000280
2	crippled	0.0000155
1	crisis	0.0001748
2	crisis	0.0003572
1	criteria	0.0000000
2	criteria	0.0000351
1	critic	0.0000001
2	critic	0.0000817
1	critical	0.0001530
2	critical	0.0001231
1	critically	0.0000502
2	critically	0.0000000
1	criticism	0.0000024
2	criticism	0.0003061
1	criticize	0.0000000
2	criticize	0.0000584
1	criticized	0.0000000
2	criticized	0.0003428
1	criticizing	0.0000000
2	criticizing	0.0000506
1	critics	0.0000048
2	critics	0.0002810
1	crocodile	0.0000502
2	crocodile	0.0000000
1	crop	0.0003182
2	crop	0.0000000
1	crops	0.0001376
2	crops	0.0000091
1	cross	0.0001890
2	cross	0.0001369
1	crossed	0.0001026
2	crossed	0.0000297
1	crosses	0.0000000
2	crosses	0.0000312
1	crossexamination	0.0000000
2	crossexamination	0.0000234
1	crossing	0.0000605
2	crossing	0.0000046
1	crowd	0.0000649
2	crowd	0.0003287
1	crowded	0.0000606
2	crowded	0.0000356
1	crowds	0.0000091
2	crowds	0.0000482
1	crowe	0.0000000
2	crowe	0.0000429
1	crown	0.0000276
2	crown	0.0000548
1	crucial	0.0000427
2	crucial	0.0000793
1	crude	0.0003540
2	crude	0.0000140
1	cruel	0.0000133
2	cruel	0.0000531
1	cruise	0.0000392
2	cruise	0.0000116
1	cruiser	0.0000502
2	cruiser	0.0000000
1	cruises	0.0000335
2	cruises	0.0000000
1	crumbling	0.0000030
2	crumbling	0.0000525
1	crunch	0.0000309
2	crunch	0.0000174
1	crusade	0.0000000
2	crusade	0.0000429
1	crush	0.0000130
2	crush	0.0000143
1	crushed	0.0000322
2	crushed	0.0000555
1	cruz	0.0000245
2	cruz	0.0000180
1	cruzan	0.0000000
2	cruzan	0.0000273
1	cry	0.0000039
2	cry	0.0000557
1	crying	0.0000236
2	crying	0.0000108
1	crystal	0.0000105
2	crystal	0.0000433
1	ctk	0.0000000
2	ctk	0.0000506
1	cuba	0.0000000
2	cuba	0.0002376
1	cuban	0.0000031
2	cuban	0.0002199
1	cubans	0.0000000
2	cubans	0.0000935
1	cubas	0.0000129
2	cubas	0.0000261
1	cubic	0.0000389
2	cubic	0.0000001
1	cuellar	0.0000000
2	cuellar	0.0000545
1	cuito	0.0000000
2	cuito	0.0000312
1	cult	0.0000000
2	cult	0.0000273
1	cultural	0.0000143
2	cultural	0.0001342
1	culture	0.0000000
2	culture	0.0001091
1	cuomo	0.0000000
2	cuomo	0.0001247
1	cuomos	0.0000000
2	cuomos	0.0000351
1	cup	0.0000002
2	cup	0.0000272
1	curator	0.0000320
2	curator	0.0000166
1	curb	0.0000534
2	curb	0.0000484
1	curbs	0.0000351
2	curbs	0.0000066
1	cure	0.0000292
2	cure	0.0000380
1	curfew	0.0000929
2	curfew	0.0000715
1	curfews	0.0000008
2	curfews	0.0000345
1	curiosity	0.0000172
2	curiosity	0.0000192
1	curious	0.0000024
2	curious	0.0000256
1	curran	0.0000502
2	curran	0.0000000
1	currencies	0.0001507
2	currencies	0.0000000
1	currency	0.0003154
2	currency	0.0000370
1	current	0.0003948
2	current	0.0003439
1	currently	0.0001553
2	currently	0.0000903
1	curriculum	0.0000000
2	curriculum	0.0000312
1	curtain	0.0000038
2	curtain	0.0000558
1	curtis	0.0000151
2	curtis	0.0000245
1	custody	0.0000230
2	custody	0.0001476
1	custom	0.0000000
2	custom	0.0000506
1	customer	0.0001563
2	customer	0.0000000
1	customers	0.0004297
2	customers	0.0000001
1	customs	0.0002356
2	customs	0.0000537
1	cut	0.0006640
2	cut	0.0003781
1	cutbacks	0.0000156
2	cutbacks	0.0000242
1	cuts	0.0001491
2	cuts	0.0002427
1	cutter	0.0000391
2	cutter	0.0000000
1	cutting	0.0001666
2	cutting	0.0000668
1	cxcbt	0.0000391
2	cxcbt	0.0000000
1	cycle	0.0000265
2	cycle	0.0000438
1	cynthia	0.0000117
2	cynthia	0.0000230
1	cypress	0.0000141
2	cypress	0.0000252
1	cyprus	0.0000030
2	cyprus	0.0000563
1	czechoslovak	0.0000000
2	czechoslovak	0.0000740
1	czechoslovakia	0.0000000
2	czechoslovakia	0.0001403
1	czechoslovakias	0.0000000
2	czechoslovakias	0.0000234
1	d	0.0001323
2	d	0.0001765
1	dad	0.0000106
2	dad	0.0000276
1	daily	0.0003129
2	daily	0.0003154
1	dairy	0.0000781
2	dairy	0.0000078
1	daisy	0.0000000
2	daisy	0.0000351
1	dakota	0.0001253
2	dakota	0.0000762
1	dalai	0.0000000
2	dalai	0.0000234
1	dale	0.0000555
2	dale	0.0000003
1	dallas	0.0001490
2	dallas	0.0000752
1	daly	0.0000335
2	daly	0.0000000
1	dam	0.0001395
2	dam	0.0000000
1	damage	0.0006524
2	damage	0.0001018
1	damaged	0.0002583
2	damaged	0.0000145
1	damages	0.0000569
2	damages	0.0001395
1	damaging	0.0000153
2	damaging	0.0000244
1	damato	0.0000000
2	damato	0.0000312
1	dame	0.0000002
2	dame	0.0000311
1	dan	0.0000214
2	dan	0.0002149
1	dance	0.0000000
2	dance	0.0001364
1	danced	0.0000335
2	danced	0.0000000
1	dancer	0.0000391
2	dancer	0.0000000
1	dances	0.0000000
2	dances	0.0000506
1	dancing	0.0000010
2	dancing	0.0000811
1	danger	0.0001156
2	danger	0.0001141
1	dangerous	0.0001002
2	dangerous	0.0001326
1	dangers	0.0000397
2	dangers	0.0000190
1	daniel	0.0000480
2	daniel	0.0001146
1	daniels	0.0000000
2	daniels	0.0000351
1	danny	0.0000154
2	danny	0.0000165
1	dariz	0.0000000
2	dariz	0.0000234
1	dark	0.0000549
2	dark	0.0000786
1	darkness	0.0000167
2	darkness	0.0000117
1	darman	0.0000000
2	darman	0.0000740
1	dartmouth	0.0000000
2	dartmouth	0.0000273
1	dash	0.0000475
2	dash	0.0000058
1	data	0.0004391
2	data	0.0000169
1	date	0.0002004
2	date	0.0003315
1	dated	0.0000188
2	dated	0.0000102
1	dates	0.0000336
2	dates	0.0000662
1	dating	0.0000019
2	dating	0.0000493
1	daughter	0.0000204
2	daughter	0.0003364
1	daughters	0.0000140
2	daughters	0.0000837
1	dave	0.0000540
2	dave	0.0000247
1	david	0.0002747
2	david	0.0003771
1	davis	0.0002984
2	davis	0.0000372
1	dawkins	0.0000000
2	dawkins	0.0000234
1	dawn	0.0000731
2	dawn	0.0000230
1	day	0.0017817
2	day	0.0014835
1	daylight	0.0000614
2	daylight	0.0000000
1	days	0.0011650
2	days	0.0009244
1	dc	0.0002182
2	dc	0.0000893
1	dcalif	0.0000000
2	dcalif	0.0000857
1	dcolo	0.0000000
2	dcolo	0.0000273
1	de	0.0000790
2	de	0.0006539
1	dea	0.0000000
2	dea	0.0000468
1	dead	0.0003539
2	dead	0.0002400
1	deadline	0.0000746
2	deadline	0.0001583
1	deadlines	0.0000237
2	deadlines	0.0000146
1	deadlocked	0.0000000
2	deadlocked	0.0000273
1	deadly	0.0000837
2	deadly	0.0000000
1	deal	0.0006053
2	deal	0.0003411
1	dealer	0.0001814
2	dealer	0.0000253
1	dealers	0.0004817
2	dealers	0.0000222
1	dealership	0.0000000
2	dealership	0.0000273
1	dealing	0.0000030
2	dealing	0.0001771
1	dealings	0.0000628
2	dealings	0.0000458
1	deals	0.0001362
2	deals	0.0000608
1	dealt	0.0000368
2	dealt	0.0000600
1	dean	0.0001214
2	dean	0.0000243
1	death	0.0002226
2	death	0.0011342
1	deaths	0.0002182
2	deaths	0.0001867
1	debate	0.0000000
2	debate	0.0004363
1	debated	0.0000000
2	debated	0.0000312
1	debates	0.0000000
2	debates	0.0000273
1	debating	0.0000110
2	debating	0.0000274
1	deborah	0.0000362
2	deborah	0.0000098
1	debra	0.0000044
2	debra	0.0000203
1	debris	0.0000837
2	debris	0.0000000
1	debt	0.0006111
2	debt	0.0000410
1	debtor	0.0000363
2	debtor	0.0000020
1	debts	0.0000855
2	debts	0.0000416
1	debut	0.0000641
2	debut	0.0000059
1	dec	0.0003001
2	dec	0.0003087
1	decade	0.0001610
2	decade	0.0001564
1	decades	0.0000503
2	decades	0.0001597
1	december	0.0003718
2	december	0.0003054
1	decembers	0.0000202
2	decembers	0.0000132
1	decide	0.0000817
2	decide	0.0001962
1	decided	0.0002135
2	decided	0.0003847
1	decides	0.0000158
2	decides	0.0000396
1	deciding	0.0000000
2	deciding	0.0000506
1	decision	0.0001594
2	decision	0.0011471
1	decisions	0.0000664
2	decisions	0.0002614
1	decisive	0.0000000
2	decisive	0.0000545
1	deck	0.0000837
2	deck	0.0000000
1	declaration	0.0000007
2	declaration	0.0001281
1	declare	0.0000251
2	declare	0.0000643
1	declared	0.0001453
2	declared	0.0002258
1	declaring	0.0000000
2	declaring	0.0000623
1	decline	0.0006205
2	decline	0.0000305
1	declined	0.0005162
2	declined	0.0002357
1	declines	0.0001954
2	declines	0.0000000
1	declining	0.0002326
2	declining	0.0000208
1	deconcini	0.0000000
2	deconcini	0.0001247
1	decorated	0.0000196
2	decorated	0.0000253
1	decrease	0.0000781
2	decrease	0.0000000
1	decree	0.0000001
2	decree	0.0001636
1	dedicated	0.0000299
2	dedicated	0.0000376
1	deductible	0.0000335
2	deductible	0.0000000
1	deduction	0.0000000
2	deduction	0.0000506
1	deductions	0.0000376
2	deductions	0.0000205
1	deemed	0.0000106
2	deemed	0.0000394
1	deep	0.0001193
2	deep	0.0001154
1	deeply	0.0000018
2	deeply	0.0000844
1	deer	0.0000673
2	deer	0.0000232
1	default	0.0000213
2	default	0.0000241
1	defeat	0.0000000
2	defeat	0.0001247
1	defeated	0.0000000
2	defeated	0.0001325
1	defeats	0.0000000
2	defeats	0.0000312
1	defect	0.0000285
2	defect	0.0000191
1	defects	0.0000486
2	defects	0.0000284
1	defend	0.0000001
2	defend	0.0001168
1	defendant	0.0000000
2	defendant	0.0001325
1	defendants	0.0000000
2	defendants	0.0001987
1	defended	0.0000274
2	defended	0.0001016
1	defending	0.0000058
2	defending	0.0000466
1	defense	0.0003536
2	defense	0.0016310
1	defensive	0.0000114
2	defensive	0.0000310
1	deferred	0.0000072
2	deferred	0.0000184
1	defiance	0.0000000
2	defiance	0.0000312
1	deficiencies	0.0000390
2	deficiencies	0.0000001
1	deficiency	0.0001312
2	deficiency	0.0000019
1	deficit	0.0005829
2	deficit	0.0002437
1	deficitreduction	0.0000478
2	deficitreduction	0.0000406
1	deficits	0.0000375
2	deficits	0.0000245
1	defied	0.0000113
2	defied	0.0000389
1	defined	0.0000848
2	defined	0.0000226
1	definite	0.0000120
2	definite	0.0000150
1	definitely	0.0000476
2	definitely	0.0000174
1	definition	0.0000176
2	definition	0.0000111
1	definitive	0.0000281
2	definitive	0.0000310
1	defrauding	0.0000155
2	defrauding	0.0000125
1	defy	0.0000150
2	defy	0.0000129
1	degree	0.0001315
2	degree	0.0000913
1	degrees	0.0004306
2	degrees	0.0000150
1	deicing	0.0000335
2	deicing	0.0000000
1	del	0.0000924
2	del	0.0000056
1	delaware	0.0000682
2	delaware	0.0000264
1	delay	0.0000874
2	delay	0.0001728
1	delayed	0.0001452
2	delayed	0.0000700
1	delaying	0.0000078
2	delaying	0.0000335
1	delays	0.0000683
2	delays	0.0000263
1	delegate	0.0000000
2	delegate	0.0000935
1	delegates	0.0000000
2	delegates	0.0004324
1	delegation	0.0000000
2	delegation	0.0002532
1	delegations	0.0000000
2	delegations	0.0000351
1	delhi	0.0000502
2	delhi	0.0000000
1	deliberate	0.0000154
2	deliberate	0.0000165
1	deliberately	0.0000000
2	deliberately	0.0000312
1	deliberations	0.0000000
2	deliberations	0.0000506
1	delicate	0.0000001
2	delicate	0.0000233
1	deliver	0.0000561
2	deliver	0.0000816
1	delivered	0.0000616
2	delivered	0.0001362
1	deliveries	0.0000706
2	deliveries	0.0000014
1	delivering	0.0000265
2	delivering	0.0000204
1	delivery	0.0003183
2	delivery	0.0000077
1	dellums	0.0000000
2	dellums	0.0000351
1	delta	0.0000893
2	delta	0.0000000
1	delvalle	0.0000000
2	delvalle	0.0000584
1	demand	0.0004674
2	demand	0.0001880
1	demanded	0.0000345
2	demanded	0.0002759
1	demanding	0.0000487
2	demanding	0.0001608
1	demands	0.0000625
2	demands	0.0002447
1	demise	0.0000206
2	demise	0.0000090
1	demjanjuk	0.0000000
2	demjanjuk	0.0000974
1	democracies	0.0000000
2	democracies	0.0000468
1	democracy	0.0000000
2	democracy	0.0003312
1	democrat	0.0000000
2	democrat	0.0003000
1	democratic	0.0000000
2	democratic	0.0014727
1	democratization	0.0000040
2	democratization	0.0000206
1	democrats	0.0000000
2	democrats	0.0006428
1	demonstrate	0.0000142
2	demonstrate	0.0000563
1	demonstrated	0.0000320
2	demonstrated	0.0000634
1	demonstration	0.0000086
2	demonstration	0.0000992
1	demonstrations	0.0000000
2	demonstrations	0.0002026
1	demonstrators	0.0000208
2	demonstrators	0.0003244
1	demoted	0.0000001
2	demoted	0.0000233
1	denial	0.0000022
2	denial	0.0000335
1	denied	0.0000393
2	denied	0.0005024
1	denies	0.0000065
2	denies	0.0000539
1	denmark	0.0000067
2	denmark	0.0000265
1	dennis	0.0000502
2	dennis	0.0000857
1	dennys	0.0000447
2	dennys	0.0000000
1	denominations	0.0000000
2	denominations	0.0000273
1	denounced	0.0000000
2	denounced	0.0000974
1	denouncing	0.0000000
2	denouncing	0.0000273
1	dense	0.0000335
2	dense	0.0000000
1	dental	0.0000576
2	dental	0.0000338
1	denver	0.0002065
2	denver	0.0000234
1	deny	0.0000026
2	deny	0.0000800
1	denying	0.0000001
2	denying	0.0000779
1	depardieu	0.0000000
2	depardieu	0.0000312
1	department	0.0018752
2	department	0.0010014
1	departments	0.0003673
2	departments	0.0000903
1	departure	0.0000356
2	departure	0.0000686
1	depend	0.0000571
2	depend	0.0000303
1	dependent	0.0000332
2	dependent	0.0000392
1	dependents	0.0000009
2	dependents	0.0000383
1	depending	0.0000740
2	depending	0.0000379
1	depends	0.0000567
2	depends	0.0000227
1	depict	0.0000002
2	depict	0.0000232
1	depicted	0.0000068
2	depicted	0.0000265
1	deploy	0.0000148
2	deploy	0.0000208
1	deployed	0.0000847
2	deployed	0.0000188
1	deployment	0.0000557
2	deployment	0.0000975
1	deportation	0.0000002
2	deportation	0.0000544
1	deportations	0.0000000
2	deportations	0.0000312
1	deported	0.0000000
2	deported	0.0000429
1	deposed	0.0000000
2	deposed	0.0000390
1	deposit	0.0001206
2	deposit	0.0000016
1	deposition	0.0000134
2	deposition	0.0000179
1	depositors	0.0000335
2	depositors	0.0000000
1	deposits	0.0000604
2	deposits	0.0000046
1	depressed	0.0000575
2	depressed	0.0000027
1	depression	0.0000675
2	depression	0.0000036
1	deprived	0.0000000
2	deprived	0.0000390
1	depth	0.0000411
2	depth	0.0000142
1	deputies	0.0000335
2	deputies	0.0000701
1	deputy	0.0001620
2	deputy	0.0004596
1	der	0.0000000
2	der	0.0000234
1	deregulation	0.0000612
2	deregulation	0.0000001
1	deri	0.0000000
2	deri	0.0000273
1	derrick	0.0000447
2	derrick	0.0000000
1	des	0.0000614
2	des	0.0000000
1	descended	0.0000035
2	descended	0.0000288
1	descent	0.0000391
2	descent	0.0000000
1	describe	0.0000416
2	describe	0.0000372
1	described	0.0002003
2	described	0.0002965
1	describes	0.0000340
2	describes	0.0000152
1	describing	0.0000024
2	describing	0.0000256
1	desert	0.0003956
2	desert	0.0000823
1	deserted	0.0000335
2	deserted	0.0000000
1	deserts	0.0000335
2	deserts	0.0000000
1	deserve	0.0000150
2	deserve	0.0000168
1	deserved	0.0000000
2	deserved	0.0000233
1	deserves	0.0000100
2	deserves	0.0000281
1	design	0.0001908
2	design	0.0000227
1	designated	0.0000326
2	designated	0.0000591
1	designation	0.0000198
2	designation	0.0000095
1	designed	0.0002553
2	designed	0.0001997
1	designing	0.0000315
2	designing	0.0000014
1	designs	0.0000162
2	designs	0.0000120
1	desire	0.0000088
2	desire	0.0000718
1	desk	0.0000041
2	desk	0.0000984
1	desperate	0.0000231
2	desperate	0.0000267
1	desperately	0.0000058
2	desperately	0.0000193
1	despite	0.0003488
2	despite	0.0003955
1	destined	0.0000332
2	destined	0.0000080
1	destiny	0.0000075
2	destiny	0.0000182
1	destroy	0.0000000
2	destroy	0.0000857
1	destroyed	0.0002715
2	destroyed	0.0000715
1	destroyer	0.0000371
2	destroyer	0.0000014
1	destroying	0.0000260
2	destroying	0.0000247
1	destruction	0.0000372
2	destruction	0.0000636
1	destructive	0.0000391
2	destructive	0.0000000
1	detail	0.0000320
2	detail	0.0000478
1	detailed	0.0000359
2	detailed	0.0000450
1	detailing	0.0000018
2	detailing	0.0000221
1	details	0.0002057
2	details	0.0003083
1	detained	0.0000026
2	detained	0.0001774
1	detainees	0.0000000
2	detainees	0.0000740
1	detect	0.0000669
2	detect	0.0000000
1	detected	0.0000558
2	detected	0.0000000
1	detection	0.0000347
2	detection	0.0000030
1	detective	0.0000490
2	detective	0.0000242
1	detectives	0.0000213
2	detectives	0.0000202
1	detector	0.0000053
2	detector	0.0000236
1	detention	0.0000000
2	detention	0.0000623
1	deter	0.0000000
2	deter	0.0000429
1	deteriorate	0.0000078
2	deteriorate	0.0000179
1	deteriorating	0.0000296
2	deteriorating	0.0000183
1	deterioration	0.0000515
2	deterioration	0.0000225
1	determination	0.0000000
2	determination	0.0000312
1	determine	0.0002500
2	determine	0.0001684
1	determined	0.0001249
2	determined	0.0000999
1	determining	0.0000602
2	determining	0.0000009
1	deterrent	0.0000000
2	deterrent	0.0000506
1	detroit	0.0001428
2	detroit	0.0001029
1	detroits	0.0000156
2	detroits	0.0000125
1	deukmejian	0.0000042
2	deukmejian	0.0000205
1	devastated	0.0000282
2	devastated	0.0000115
1	devastating	0.0000218
2	devastating	0.0000237
1	develop	0.0001917
2	develop	0.0000221
1	developed	0.0002218
2	developed	0.0000361
1	developer	0.0000000
2	developer	0.0000584
1	developers	0.0000134
2	developers	0.0000296
1	developing	0.0001512
2	developing	0.0000503
1	development	0.0004780
2	development	0.0002508
1	developments	0.0000764
2	developments	0.0000830
1	device	0.0000414
2	device	0.0001347
1	devices	0.0001805
2	devices	0.0000182
1	devoted	0.0000073
2	devoted	0.0000456
1	dfla	0.0000000
2	dfla	0.0000312
1	dga	0.0000000
2	dga	0.0000506
1	dhaka	0.0000949
2	dhaka	0.0000000
1	diagnosed	0.0000272
2	diagnosed	0.0000278
1	diagnosis	0.0000000
2	diagnosis	0.0000234
1	dialogue	0.0000000
2	dialogue	0.0001169
1	diamond	0.0000846
2	diamond	0.0000072
1	diana	0.0000346
2	diana	0.0000070
1	diapers	0.0000669
2	diapers	0.0000000
1	diaries	0.0000019
2	diaries	0.0000298
1	dick	0.0000636
2	dick	0.0000881
1	dickey	0.0000000
2	dickey	0.0000273
1	dickman	0.0000447
2	dickman	0.0000000
1	dictate	0.0000000
2	dictate	0.0000234
1	dictated	0.0000048
2	dictated	0.0000317
1	dictator	0.0000000
2	dictator	0.0000779
1	dictators	0.0000000
2	dictators	0.0000273
1	dictatorship	0.0000000
2	dictatorship	0.0000506
1	didnt	0.0003668
2	didnt	0.0006166
1	die	0.0000886
2	die	0.0001680
1	died	0.0005573
2	died	0.0007642
1	diego	0.0001479
2	diego	0.0000292
1	dies	0.0000101
2	dies	0.0000163
1	diestel	0.0000000
2	diestel	0.0000234
1	diet	0.0000815
2	diet	0.0000327
1	differ	0.0000072
2	differ	0.0000417
1	differed	0.0000168
2	differed	0.0000116
1	difference	0.0001263
2	difference	0.0001222
1	differences	0.0000797
2	differences	0.0001547
1	different	0.0003258
2	different	0.0003219
1	differently	0.0000172
2	differently	0.0000114
1	difficult	0.0002653
2	difficult	0.0002629
1	difficulties	0.0000410
2	difficulties	0.0000142
1	difficulty	0.0000741
2	difficulty	0.0000457
1	digging	0.0000001
2	digging	0.0000350
1	digital	0.0001172
2	digital	0.0000000
1	digits	0.0000525
2	digits	0.0000062
1	dignity	0.0000000
2	dignity	0.0000468
1	dill	0.0000027
2	dill	0.0000566
1	diller	0.0000219
2	diller	0.0000081
1	dilorenzo	0.0000000
2	dilorenzo	0.0000312
1	diminished	0.0000371
2	diminished	0.0000169
1	dingell	0.0000001
2	dingell	0.0000467
1	dining	0.0000292
2	dining	0.0000186
1	dinner	0.0000067
2	dinner	0.0001628
1	dioxide	0.0000819
2	dioxide	0.0000168
1	dip	0.0000630
2	dip	0.0000028
1	diplomacy	0.0000000
2	diplomacy	0.0000234
1	diplomat	0.0000001
2	diplomat	0.0000895
1	diplomatic	0.0000112
2	diplomatic	0.0002571
1	diplomats	0.0000061
2	diplomats	0.0002451
1	direct	0.0000499
2	direct	0.0002495
1	directed	0.0000759
2	directed	0.0000912
1	directing	0.0000004
2	directing	0.0000270
1	direction	0.0001850
2	direction	0.0000618
1	directions	0.0000338
2	directions	0.0000154
1	directly	0.0001115
2	directly	0.0001832
1	director	0.0008143
2	director	0.0007484
1	directorate	0.0000000
2	directorate	0.0000351
1	directors	0.0001684
2	directors	0.0000578
1	directory	0.0000135
2	directory	0.0000139
1	dirt	0.0000404
2	dirt	0.0000146
1	dirty	0.0000000
2	dirty	0.0000584
1	disability	0.0000000
2	disability	0.0000429
1	disabled	0.0000251
2	disabled	0.0000137
1	disagree	0.0000002
2	disagree	0.0000466
1	disagreed	0.0000271
2	disagreed	0.0000317
1	disagreement	0.0000006
2	disagreement	0.0000425
1	disappear	0.0000186
2	disappear	0.0000104
1	disappearance	0.0000184
2	disappearance	0.0000222
1	disappeared	0.0000592
2	disappeared	0.0000444
1	disappearing	0.0000271
2	disappearing	0.0000044
1	disappointed	0.0000342
2	disappointed	0.0000735
1	disappointing	0.0000452
2	disappointing	0.0000074
1	disappointment	0.0000376
2	disappointment	0.0000127
1	disapproved	0.0000004
2	disapproved	0.0000231
1	disarm	0.0000000
2	disarm	0.0000273
1	disarmament	0.0000000
2	disarmament	0.0000351
1	disarray	0.0000137
2	disarray	0.0000177
1	disaster	0.0002398
2	disaster	0.0000508
1	disasters	0.0000360
2	disasters	0.0000061
1	disbanded	0.0000000
2	disbanded	0.0000312
1	disc	0.0000506
2	disc	0.0000154
1	discharge	0.0000229
2	discharge	0.0000191
1	discharged	0.0000445
2	discharged	0.0000001
1	disciples	0.0000074
2	disciples	0.0000221
1	disciplinary	0.0000025
2	disciplinary	0.0000411
1	discipline	0.0000000
2	discipline	0.0000312
1	disclose	0.0000511
2	disclose	0.0000968
1	disclosed	0.0001097
2	disclosed	0.0001221
1	disclosing	0.0000000
2	disclosing	0.0000312
1	disclosure	0.0000445
2	disclosure	0.0001092
1	discontent	0.0000134
2	discontent	0.0000179
1	discontinued	0.0000502
2	discontinued	0.0000000
1	discount	0.0001786
2	discount	0.0000000
1	discounted	0.0000443
2	discounted	0.0000041
1	discourage	0.0000097
2	discourage	0.0000322
1	discouraged	0.0000515
2	discouraged	0.0000069
1	discouraging	0.0000314
2	discouraging	0.0000132
1	discovered	0.0002540
2	discovered	0.0000682
1	discovery	0.0001283
2	discovery	0.0000312
1	discoverys	0.0000447
2	discoverys	0.0000000
1	discredit	0.0000000
2	discredit	0.0000234
1	discretion	0.0000011
2	discretion	0.0000343
1	discretionary	0.0000068
2	discretionary	0.0000186
1	discrimination	0.0000000
2	discrimination	0.0001558
1	discriminatory	0.0000005
2	discriminatory	0.0000230
1	discuss	0.0000372
2	discuss	0.0003792
1	discussed	0.0000020
2	discussed	0.0002480
1	discussing	0.0000296
2	discussing	0.0000494
1	discussion	0.0000000
2	discussion	0.0001325
1	discussions	0.0000204
2	discussions	0.0001650
1	disease	0.0004275
2	disease	0.0001107
1	diseases	0.0001144
2	diseases	0.0000370
1	disguise	0.0000026
2	disguise	0.0000216
1	dish	0.0000335
2	dish	0.0000000
1	dishes	0.0000558
2	dishes	0.0000000
1	diskin	0.0000391
2	diskin	0.0000000
1	disks	0.0000502
2	disks	0.0000000
1	dismantled	0.0000211
2	dismantled	0.0000243
1	dismiss	0.0000000
2	dismiss	0.0000390
1	dismissal	0.0000000
2	dismissal	0.0000390
1	dismissed	0.0000532
2	dismissed	0.0001265
1	dismissing	0.0000000
2	dismissing	0.0000429
1	disney	0.0001842
2	disney	0.0000000
1	disneys	0.0000502
2	disneys	0.0000000
1	disorder	0.0000000
2	disorder	0.0000390
1	disorderly	0.0000001
2	disorderly	0.0000272
1	dispatch	0.0000105
2	dispatch	0.0000395
1	dispatched	0.0000286
2	dispatched	0.0000307
1	dispatcher	0.0000256
2	dispatcher	0.0000133
1	disperse	0.0000161
2	disperse	0.0000355
1	dispersed	0.0000065
2	dispersed	0.0000383
1	displaced	0.0000502
2	displaced	0.0000000
1	display	0.0000445
2	display	0.0000469
1	displayed	0.0000302
2	displayed	0.0000529
1	displays	0.0000000
2	displays	0.0000234
1	disposable	0.0000446
2	disposable	0.0000000
1	disposal	0.0000502
2	disposal	0.0000000
1	disposed	0.0000038
2	disposed	0.0000246
1	dispute	0.0000005
2	dispute	0.0002996
1	disputed	0.0000221
2	disputed	0.0000858
1	disputes	0.0000097
2	disputes	0.0001218
1	disqualified	0.0000057
2	disqualified	0.0000272
1	disrupt	0.0000000
2	disrupt	0.0000390
1	disrupted	0.0000279
2	disrupted	0.0000039
1	disruptive	0.0000318
2	disruptive	0.0000129
1	dissatisfaction	0.0000000
2	dissatisfaction	0.0000312
1	dissent	0.0000000
2	dissent	0.0000312
1	dissident	0.0000000
2	dissident	0.0000818
1	dissidents	0.0000000
2	dissidents	0.0000779
1	distance	0.0000369
2	distance	0.0000132
1	distant	0.0000396
2	distant	0.0000113
1	distinction	0.0000000
2	distinction	0.0000273
1	distinguished	0.0000013
2	distinguished	0.0000420
1	distress	0.0000184
2	distress	0.0000378
1	distribute	0.0000204
2	distribute	0.0000364
1	distributed	0.0000462
2	distributed	0.0000924
1	distributing	0.0000243
2	distributing	0.0000220
1	distribution	0.0002008
2	distribution	0.0000624
1	distributor	0.0000334
2	distributor	0.0000001
1	district	0.0002327
2	district	0.0008856
1	districts	0.0000235
2	districts	0.0001589
1	disturbance	0.0000390
2	disturbance	0.0000000
1	disturbances	0.0000000
2	disturbances	0.0000273
1	disturbed	0.0000154
2	disturbed	0.0000243
1	diverse	0.0000124
2	diverse	0.0000381
1	diversified	0.0000502
2	diversified	0.0000000
1	diversity	0.0000000
2	diversity	0.0000273
1	divert	0.0000147
2	divert	0.0000248
1	diverted	0.0000211
2	diverted	0.0000242
1	divided	0.0000101
2	divided	0.0001254
1	dividend	0.0001031
2	dividend	0.0000177
1	dividends	0.0000670
2	dividends	0.0000000
1	dividing	0.0000000
2	dividing	0.0000234
1	diving	0.0000502
2	diving	0.0000000
1	division	0.0003787
2	division	0.0000707
1	divisions	0.0000899
2	divisions	0.0000269
1	divorce	0.0000000
2	divorce	0.0000740
1	divorced	0.0000001
2	divorced	0.0000311
1	dixon	0.0000000
2	dixon	0.0000896
1	dmaine	0.0000000
2	dmaine	0.0000390
1	dmass	0.0000000
2	dmass	0.0000662
1	dmich	0.0000133
2	dmich	0.0000609
1	dna	0.0000000
2	dna	0.0000429
1	dny	0.0000000
2	dny	0.0000234
1	doc	0.0000000
2	doc	0.0000234
1	dock	0.0000614
2	dock	0.0000000
1	doctor	0.0001082
2	doctor	0.0000959
1	doctors	0.0001527
2	doctors	0.0002830
1	document	0.0000000
2	document	0.0001558
1	documentary	0.0000002
2	documentary	0.0000427
1	documented	0.0000221
2	documented	0.0000118
1	documentmatching	0.0000447
2	documentmatching	0.0000000
1	documents	0.0000223
2	documents	0.0004636
1	dodd	0.0000000
2	dodd	0.0000234
1	dodge	0.0000246
2	dodge	0.0000101
1	doesnt	0.0002838
2	doesnt	0.0003512
1	dog	0.0001439
2	dog	0.0000437
1	dogs	0.0000972
2	dogs	0.0000140
1	dohio	0.0000000
2	dohio	0.0000429
1	doing	0.0002215
2	doing	0.0003324
1	dole	0.0000000
2	dole	0.0003039
1	doles	0.0000000
2	doles	0.0000545
1	dollar	0.0017638
2	dollar	0.0000000
1	dollars	0.0007541
2	dollars	0.0000775
1	domestic	0.0004477
2	domestic	0.0001005
1	dominance	0.0000311
2	dominance	0.0000172
1	dominant	0.0000116
2	dominant	0.0000309
1	dominate	0.0000000
2	dominate	0.0000273
1	dominated	0.0000269
2	dominated	0.0000942
1	domination	0.0000001
2	domination	0.0000350
1	dominican	0.0000078
2	dominican	0.0000569
1	don	0.0000469
2	don	0.0000842
1	donald	0.0001482
2	donald	0.0001381
1	donate	0.0000000
2	donate	0.0000234
1	donated	0.0000001
2	donated	0.0000856
1	donations	0.0000000
2	donations	0.0001364
1	done	0.0002619
2	done	0.0004873
1	donna	0.0000155
2	donna	0.0000204
1	donor	0.0000000
2	donor	0.0000234
1	donors	0.0000436
2	donors	0.0000631
1	dont	0.0009603
2	dont	0.0013595
1	doomed	0.0000015
2	doomed	0.0000262
1	door	0.0001559
2	door	0.0001912
1	doors	0.0001589
2	doors	0.0000371
1	dorrance	0.0000335
2	dorrance	0.0000000
1	dose	0.0000335
2	dose	0.0000000
1	doses	0.0000002
2	doses	0.0000232
1	double	0.0001661
2	double	0.0000360
1	doubled	0.0000547
2	doubled	0.0000125
1	doubling	0.0000115
2	doubling	0.0000154
1	doubt	0.0000671
2	doubt	0.0001207
1	doubtful	0.0000210
2	doubtful	0.0000087
1	doubts	0.0000312
2	doubts	0.0000795
1	doug	0.0000499
2	doug	0.0000353
1	douglas	0.0001388
2	douglas	0.0000707
1	dow	0.0005633
2	dow	0.0000003
1	downed	0.0000614
2	downed	0.0000000
1	downing	0.0000000
2	downing	0.0000234
1	downstream	0.0000391
2	downstream	0.0000000
1	downtown	0.0002674
2	downtown	0.0000705
1	downturn	0.0000391
2	downturn	0.0000000
1	downward	0.0000837
2	downward	0.0000000
1	doyle	0.0000335
2	doyle	0.0000000
1	dozen	0.0001577
2	dozen	0.0001704
1	dozens	0.0000979
2	dozens	0.0001460
1	dr	0.0003176
2	dr	0.0002029
1	draft	0.0000412
2	draft	0.0002167
1	drafted	0.0000045
2	drafted	0.0000397
1	drafting	0.0000000
2	drafting	0.0000273
1	drag	0.0000435
2	drag	0.0000086
1	dragged	0.0000379
2	dragged	0.0000281
1	drain	0.0000528
2	drain	0.0000099
1	drake	0.0000391
2	drake	0.0000000
1	drama	0.0000016
2	drama	0.0000846
1	dramatic	0.0000274
2	dramatic	0.0001133
1	dramatically	0.0000202
2	dramatically	0.0000249
1	drank	0.0000403
2	drank	0.0000070
1	draped	0.0000006
2	draped	0.0000268
1	drastic	0.0000001
2	drastic	0.0000389
1	drastically	0.0000283
2	drastically	0.0000153
1	draw	0.0000208
2	draw	0.0001180
1	drawing	0.0000891
2	drawing	0.0000430
1	drawn	0.0000424
2	drawn	0.0001028
1	draws	0.0000000
2	draws	0.0000506
1	dream	0.0000004
2	dream	0.0000854
1	dreams	0.0000146
2	dreams	0.0000171
1	dress	0.0000095
2	dress	0.0000791
1	dressed	0.0000099
2	dressed	0.0000788
1	dresses	0.0000000
2	dresses	0.0000974
1	drew	0.0000423
2	drew	0.0001302
1	drexel	0.0002260
2	drexel	0.0000370
1	drexels	0.0000000
2	drexels	0.0000312
1	dried	0.0000234
2	dried	0.0000070
1	drifted	0.0000531
2	drifted	0.0000058
1	drill	0.0000502
2	drill	0.0000000
1	drilling	0.0000949
2	drilling	0.0000000
1	drink	0.0000384
2	drink	0.0000161
1	drinking	0.0002051
2	drinking	0.0000205
1	drinks	0.0000254
2	drinks	0.0000135
1	drive	0.0001471
2	drive	0.0001973
1	driven	0.0000849
2	driven	0.0000537
1	driver	0.0002790
2	driver	0.0000000
1	drivers	0.0002035
2	drivers	0.0000021
1	drives	0.0000058
2	drives	0.0000388
1	driveway	0.0000349
2	driveway	0.0000068
1	driving	0.0002565
2	driving	0.0001248
1	drop	0.0004585
2	drop	0.0000501
1	dropout	0.0000000
2	dropout	0.0000234
1	dropped	0.0005801
2	dropped	0.0000782
1	dropping	0.0000397
2	dropping	0.0000268
1	drops	0.0000281
2	drops	0.0000077
1	drought	0.0003851
2	drought	0.0000000
1	droughtstricken	0.0000335
2	droughtstricken	0.0000000
1	drove	0.0001650
2	drove	0.0000719
1	drowned	0.0000563
2	drowned	0.0000035
1	drug	0.0004600
2	drug	0.0010659
1	drugrelated	0.0000003
2	drugrelated	0.0000232
1	drugs	0.0001529
2	drugs	0.0003803
1	drum	0.0000089
2	drum	0.0000289
1	drunk	0.0000376
2	drunk	0.0000166
1	drunken	0.0000286
2	drunken	0.0000307
1	dry	0.0002233
2	dry	0.0000000
1	dtenn	0.0000000
2	dtenn	0.0000273
1	dtexas	0.0000002
2	dtexas	0.0000505
1	du	0.0000275
2	du	0.0000314
1	dual	0.0000301
2	dual	0.0000024
1	duarte	0.0000000
2	duarte	0.0000429
1	dubbed	0.0000558
2	dubbed	0.0000000
1	duchess	0.0000726
2	duchess	0.0000000
1	duck	0.0000121
2	duck	0.0000149
1	ducks	0.0000356
2	ducks	0.0000063
1	dudycz	0.0000000
2	dudycz	0.0000273
1	due	0.0004232
2	due	0.0001293
1	duffy	0.0000000
2	duffy	0.0000234
1	dug	0.0000726
2	dug	0.0000000
1	dukakis	0.0000000
2	dukakis	0.0017493
1	dukakisbentsen	0.0000000
2	dukakisbentsen	0.0000234
1	duke	0.0000266
2	duke	0.0000321
1	dull	0.0000391
2	dull	0.0000000
1	dump	0.0001006
2	dump	0.0000116
1	dumped	0.0000969
2	dumped	0.0000064
1	dumping	0.0000325
2	dumping	0.0000279
1	dumps	0.0000015
2	dumps	0.0000496
1	duncan	0.0000335
2	duncan	0.0000000
1	dunn	0.0000001
2	dunn	0.0000272
1	duo	0.0000000
2	duo	0.0000273
1	durable	0.0000781
2	durable	0.0000000
1	duracell	0.0000726
2	duracell	0.0000000
1	durenberger	0.0000000
2	durenberger	0.0000701
1	dust	0.0000366
2	dust	0.0000173
1	dutch	0.0002177
2	dutch	0.0000000
1	duties	0.0000242
2	duties	0.0000727
1	duty	0.0002133
2	duty	0.0000615
1	duvalier	0.0000000
2	duvalier	0.0000506
1	dvt	0.0000014
2	dvt	0.0000341
1	dwash	0.0000000
2	dwash	0.0000273
1	dwayne	0.0000000
2	dwayne	0.0000234
1	dwis	0.0000000
2	dwis	0.0000429
1	dwva	0.0000014
2	dwva	0.0000302
1	dying	0.0000004
2	dying	0.0000621
1	dyke	0.0000000
2	dyke	0.0000273
1	dylan	0.0000000
2	dylan	0.0000273
1	dynamic	0.0000000
2	dynamic	0.0000234
1	dynamics	0.0000638
2	dynamics	0.0000061
1	e	0.0001452
2	e	0.0001831
1	eager	0.0000005
2	eager	0.0000698
1	eagle	0.0000252
2	eagle	0.0000058
1	ear	0.0000372
2	ear	0.0000364
1	earl	0.0000447
2	earl	0.0000272
1	earlier	0.0010603
2	earlier	0.0009079
1	early	0.0014710
2	early	0.0005550
1	earmarked	0.0000214
2	earmarked	0.0000318
1	earn	0.0001073
2	earn	0.0000069
1	earned	0.0002120
2	earned	0.0000546
1	earning	0.0000410
2	earning	0.0000064
1	earnings	0.0005637
2	earnings	0.0000000
1	earrings	0.0000000
2	earrings	0.0000273
1	ears	0.0000317
2	ears	0.0000013
1	earth	0.0003358
2	earth	0.0000189
1	earthquake	0.0002732
2	earthquake	0.0000002
1	earthquakes	0.0000726
2	earthquakes	0.0000000
1	earths	0.0000837
2	earths	0.0000000
1	ease	0.0000913
2	ease	0.0000805
1	eased	0.0000565
2	eased	0.0000151
1	easier	0.0000402
2	easier	0.0000577
1	easily	0.0000601
2	easily	0.0000827
1	easing	0.0000456
2	easing	0.0000500
1	east	0.0005289
2	east	0.0012944
1	easter	0.0000000
2	easter	0.0000234
1	easterly	0.0000002
2	easterly	0.0000232
1	eastern	0.0007405
2	eastern	0.0004026
1	easterns	0.0001061
2	easterns	0.0000000
1	eastwest	0.0000000
2	eastwest	0.0000740
1	easy	0.0000405
2	easy	0.0000730
1	eat	0.0001451
2	eat	0.0000000
1	eating	0.0001004
2	eating	0.0000000
1	ec	0.0000000
2	ec	0.0001870
1	echoed	0.0000015
2	echoed	0.0000418
1	eckstein	0.0000000
2	eckstein	0.0000234
1	eclipses	0.0000391
2	eclipses	0.0000000
1	economic	0.0010261
2	economic	0.0011110
1	economically	0.0000103
2	economically	0.0000512
1	economics	0.0000628
2	economics	0.0000302
1	economies	0.0000293
2	economies	0.0000458
1	economist	0.0003169
2	economist	0.0000087
1	economists	0.0003461
2	economists	0.0000000
1	economy	0.0009850
2	economy	0.0004540
1	ecs	0.0000000
2	ecs	0.0000312
1	ed	0.0000495
2	ed	0.0000590
1	eddie	0.0000175
2	eddie	0.0000229
1	edgar	0.0000000
2	edgar	0.0000273
1	edge	0.0000979
2	edge	0.0000368
1	edged	0.0001023
2	edged	0.0000104
1	edgemont	0.0000726
2	edgemont	0.0000000
1	edges	0.0000186
2	edges	0.0000182
1	edison	0.0000502
2	edison	0.0000000
1	edition	0.0000234
2	edition	0.0000616
1	editions	0.0000219
2	editions	0.0000704
1	editor	0.0000551
2	editor	0.0002694
1	editorial	0.0000014
2	editorial	0.0000809
1	editors	0.0000002
2	editors	0.0001051
1	edt	0.0001895
2	edt	0.0000080
1	eduard	0.0000025
2	eduard	0.0000606
1	eduardo	0.0000000
2	eduardo	0.0000468
1	educate	0.0000156
2	educate	0.0000242
1	educated	0.0000000
2	educated	0.0000273
1	education	0.0000146
2	education	0.0005586
1	educational	0.0000315
2	educational	0.0000560
1	edward	0.0000493
2	edward	0.0002188
1	edwards	0.0000451
2	edwards	0.0000270
1	edwin	0.0000286
2	edwin	0.0001320
1	eec	0.0000000
2	eec	0.0000779
1	effect	0.0004061
2	effect	0.0002932
1	effective	0.0002317
2	effective	0.0000837
1	effectively	0.0000387
2	effectively	0.0000782
1	effects	0.0001713
2	effects	0.0000324
1	efficiency	0.0000391
2	efficiency	0.0000000
1	efficient	0.0000585
2	efficient	0.0000059
1	efficiently	0.0000389
2	efficiently	0.0000001
1	effort	0.0002601
2	effort	0.0004496
1	efforts	0.0002094
2	efforts	0.0004577
1	egg	0.0000390
2	egg	0.0000000
1	eggs	0.0001090
2	eggs	0.0000135
1	egypt	0.0000000
2	egypt	0.0001675
1	egyptian	0.0000012
2	egyptian	0.0001044
1	egyptians	0.0000000
2	egyptians	0.0000351
1	egypts	0.0000000
2	egypts	0.0000312
1	ehrenhalt	0.0000335
2	ehrenhalt	0.0000000
1	eight	0.0005768
2	eight	0.0003999
1	eighth	0.0000487
2	eighth	0.0000088
1	eighthour	0.0000191
2	eighthour	0.0000100
1	eighty	0.0000447
2	eighty	0.0000000
1	eightyear	0.0000000
2	eightyear	0.0000390
1	el	0.0000290
2	el	0.0001590
1	elaborate	0.0000541
2	elaborate	0.0001064
1	elaine	0.0000000
2	elaine	0.0000234
1	elder	0.0000001
2	elder	0.0000311
1	elderly	0.0000499
2	elderly	0.0000976
1	elect	0.0000001
2	elect	0.0000583
1	elected	0.0000224
2	elected	0.0004480
1	election	0.0000319
2	election	0.0008894
1	elections	0.0000099
2	elections	0.0007411
1	electionyear	0.0000000
2	electionyear	0.0000312
1	electoral	0.0000000
2	electoral	0.0001792
1	electorate	0.0000000
2	electorate	0.0000273
1	electric	0.0004167
2	electric	0.0000442
1	electrical	0.0001507
2	electrical	0.0000000
1	electricity	0.0002233
2	electricity	0.0000000
1	electronic	0.0002141
2	electronic	0.0000181
1	electronics	0.0000893
2	electronics	0.0000000
1	element	0.0000074
2	element	0.0000338
1	elementary	0.0000080
2	elementary	0.0000295
1	elements	0.0000000
2	elements	0.0000545
1	elephant	0.0000558
2	elephant	0.0000000
1	elephants	0.0000781
2	elephants	0.0000000
1	elevations	0.0000447
2	elevations	0.0000000
1	elevator	0.0000502
2	elevator	0.0000000
1	eleven	0.0000333
2	eleven	0.0000118
1	eli	0.0000001
2	eli	0.0000233
1	eligibility	0.0000000
2	eligibility	0.0000312
1	eligible	0.0000000
2	eligible	0.0001363
1	eliminate	0.0001095
2	eliminate	0.0000522
1	eliminated	0.0000472
2	eliminated	0.0000683
1	eliminating	0.0000105
2	eliminating	0.0000394
1	elimination	0.0000198
2	elimination	0.0000251
1	elite	0.0000005
2	elite	0.0000386
1	elizabeth	0.0000000
2	elizabeth	0.0001324
1	elliot	0.0000013
2	elliot	0.0000225
1	elliott	0.0000079
2	elliott	0.0000179
1	elses	0.0000008
2	elses	0.0000228
1	elvis	0.0000837
2	elvis	0.0000000
1	em	0.0000036
2	em	0.0000325
1	embargo	0.0000230
2	embargo	0.0001398
1	embarrassed	0.0000184
2	embarrassed	0.0000183
1	embassies	0.0000025
2	embassies	0.0000411
1	embassy	0.0000231
2	embassy	0.0004669
1	embraced	0.0000000
2	embraced	0.0000468
1	emerged	0.0000304
2	emerged	0.0000567
1	emergency	0.0004306
2	emergency	0.0002371
1	emerging	0.0000000
2	emerging	0.0000429
1	emigrate	0.0000000
2	emigrate	0.0000468
1	emigration	0.0000000
2	emigration	0.0000662
1	emirates	0.0000000
2	emirates	0.0000429
1	emissions	0.0001308
2	emissions	0.0000100
1	emotion	0.0000000
2	emotion	0.0000273
1	emotional	0.0000002
2	emotional	0.0000895
1	emperor	0.0000082
2	emperor	0.0000995
1	emperors	0.0000000
2	emperors	0.0000273
1	emphasis	0.0000118
2	emphasis	0.0000463
1	emphasize	0.0000117
2	emphasize	0.0000230
1	emphasized	0.0000000
2	emphasized	0.0000429
1	empire	0.0000074
2	empire	0.0000688
1	employ	0.0000391
2	employ	0.0000000
1	employed	0.0000712
2	employed	0.0000165
1	employee	0.0002064
2	employee	0.0000546
1	employees	0.0009904
2	employees	0.0002554
1	employer	0.0000011
2	employer	0.0000460
1	employers	0.0000190
2	employers	0.0001503
1	employing	0.0000180
2	employing	0.0000108
1	employment	0.0002384
2	employment	0.0000479
1	employs	0.0000837
2	employs	0.0000000
1	empty	0.0000569
2	empty	0.0000421
1	en	0.0000823
2	en	0.0000166
1	enable	0.0000616
2	enable	0.0000388
1	enact	0.0000000
2	enact	0.0000468
1	enacted	0.0000000
2	enacted	0.0000740
1	enclave	0.0000691
2	enclave	0.0000063
1	encounter	0.0000539
2	encounter	0.0000364
1	encourage	0.0000844
2	encourage	0.0001242
1	encouraged	0.0000404
2	encouraged	0.0000458
1	encouraging	0.0000552
2	encouraging	0.0000627
1	end	0.0006769
2	end	0.0010664
1	endanger	0.0000188
2	endanger	0.0000180
1	endangered	0.0001518
2	endangered	0.0000070
1	ended	0.0004798
2	ended	0.0002417
1	ending	0.0002748
2	ending	0.0000848
1	endorse	0.0000000
2	endorse	0.0000429
1	endorsed	0.0000401
2	endorsed	0.0000772
1	endorsement	0.0000062
2	endorsement	0.0000502
1	endowment	0.0000000
2	endowment	0.0000623
1	ends	0.0001040
2	ends	0.0000755
1	enduring	0.0000000
2	enduring	0.0000234
1	enemies	0.0000000
2	enemies	0.0000623
1	enemy	0.0000272
2	enemy	0.0000979
1	energy	0.0007032
2	energy	0.0000390
1	enforce	0.0000236
2	enforce	0.0001004
1	enforced	0.0000084
2	enforced	0.0000253
1	enforcement	0.0000707
2	enforcement	0.0002935
1	enforcing	0.0000104
2	enforcing	0.0000200
1	engage	0.0000068
2	engage	0.0000342
1	engaged	0.0000228
2	engaged	0.0001010
1	engine	0.0001395
2	engine	0.0000000
1	engineer	0.0001059
2	engineer	0.0000235
1	engineered	0.0000323
2	engineered	0.0000125
1	engineering	0.0001800
2	engineering	0.0000030
1	engineers	0.0001842
2	engineers	0.0000000
1	engines	0.0001061
2	engines	0.0000000
1	england	0.0003705
2	england	0.0000180
1	english	0.0000199
2	english	0.0001809
1	englishlanguage	0.0000093
2	englishlanguage	0.0000247
1	engulfed	0.0000373
2	engulfed	0.0000012
1	enhance	0.0000336
2	enhance	0.0000194
1	enjoy	0.0000345
2	enjoy	0.0000188
1	enjoyed	0.0000416
2	enjoyed	0.0000411
1	enjoying	0.0000077
2	enjoying	0.0000258
1	enlisted	0.0000146
2	enlisted	0.0000210
1	enormous	0.0000901
2	enormous	0.0000540
1	enrile	0.0000000
2	enrile	0.0000468
1	enrique	0.0000005
2	enrique	0.0000464
1	ensure	0.0000312
2	ensure	0.0001613
1	ensuring	0.0000000
2	ensuring	0.0000312
1	enter	0.0000716
2	enter	0.0001253
1	entered	0.0001656
2	entered	0.0001221
1	entering	0.0000576
2	entering	0.0000533
1	enterprise	0.0000215
2	enterprise	0.0000590
1	enterprises	0.0000479
2	enterprises	0.0000211
1	entertained	0.0000000
2	entertained	0.0000234
1	entertainer	0.0000011
2	entertainer	0.0000382
1	entertainment	0.0002092
2	entertainment	0.0000059
1	enthusiasm	0.0000245
2	enthusiasm	0.0000413
1	enthusiastic	0.0000141
2	enthusiastic	0.0000447
1	entire	0.0002971
2	entire	0.0001394
1	entirely	0.0000697
2	entirely	0.0000332
1	entitled	0.0000035
2	entitled	0.0001105
1	entrance	0.0001061
2	entrance	0.0000156
1	entrapment	0.0000019
2	entrapment	0.0000299
1	entries	0.0000080
2	entries	0.0000373
1	entry	0.0000353
2	entry	0.0000650
1	envelope	0.0000000
2	envelope	0.0000234
1	envelopes	0.0000274
2	envelopes	0.0000160
1	environment	0.0002440
2	environment	0.0000478
1	environmental	0.0007369
2	environmental	0.0000194
1	environmentalists	0.0001116
2	environmentalists	0.0000000
1	environmentally	0.0000558
2	environmentally	0.0000000
1	envoy	0.0000000
2	envoy	0.0000468
1	envoys	0.0000000
2	envoys	0.0000390
1	epa	0.0002843
2	epa	0.0000002
1	epas	0.0000391
2	epas	0.0000000
1	epic	0.0000113
2	epic	0.0000155
1	epicenter	0.0000335
2	epicenter	0.0000000
1	episode	0.0000462
2	episode	0.0000223
1	epps	0.0000391
2	epps	0.0000000
1	epstein	0.0000000
2	epstein	0.0000312
1	equal	0.0000054
2	equal	0.0001092
1	equally	0.0000195
2	equally	0.0000371
1	equals	0.0000000
2	equals	0.0000390
1	equipment	0.0006948
2	equipment	0.0000877
1	equipped	0.0000512
2	equipped	0.0000071
1	equity	0.0001106
2	equity	0.0000007
1	equivalent	0.0001050
2	equivalent	0.0000319
1	era	0.0000589
2	era	0.0000758
1	erected	0.0000339
2	erected	0.0000192
1	eric	0.0000311
2	eric	0.0000523
1	erickson	0.0000224
2	erickson	0.0000077
1	erie	0.0000558
2	erie	0.0000000
1	ernest	0.0000316
2	ernest	0.0000013
1	erode	0.0000335
2	erode	0.0000000
1	erols	0.0000335
2	erols	0.0000000
1	error	0.0000946
2	error	0.0000820
1	errors	0.0000521
2	errors	0.0000220
1	ershad	0.0000002
2	ershad	0.0000388
1	erupted	0.0000287
2	erupted	0.0000345
1	escalation	0.0000023
2	escalation	0.0000335
1	escape	0.0000915
2	escape	0.0000530
1	escaped	0.0000304
2	escaped	0.0000801
1	escorted	0.0000589
2	escorted	0.0000134
1	escorts	0.0000390
2	escorts	0.0000001
1	essay	0.0000000
2	essay	0.0000234
1	essential	0.0000586
2	essential	0.0000760
1	essentially	0.0000223
2	essentially	0.0000662
1	essentials	0.0000090
2	essentials	0.0000171
1	est	0.0001228
2	est	0.0000000
1	establish	0.0000000
2	establish	0.0001130
1	established	0.0000884
2	established	0.0002110
1	establishes	0.0000000
2	establishes	0.0000468
1	establishing	0.0000004
2	establishing	0.0000542
1	establishment	0.0000000
2	establishment	0.0000740
1	estate	0.0004278
2	estate	0.0001767
1	estates	0.0000185
2	estates	0.0000104
1	estimate	0.0003850
2	estimate	0.0000235
1	estimated	0.0006494
2	estimated	0.0001662
1	estimates	0.0003304
2	estimates	0.0000694
1	estonia	0.0000000
2	estonia	0.0000545
1	estonian	0.0000000
2	estonian	0.0000468
1	et	0.0000283
2	et	0.0000036
1	ethical	0.0000000
2	ethical	0.0000662
1	ethics	0.0000000
2	ethics	0.0001286
1	ethiopia	0.0000000
2	ethiopia	0.0000234
1	ethnic	0.0000000
2	ethnic	0.0002026
1	etruscan	0.0000000
2	etruscan	0.0000351
1	eugene	0.0000193
2	eugene	0.0000138
1	europe	0.0003148
2	europe	0.0006646
1	european	0.0002841
2	european	0.0004172
1	europeans	0.0000178
2	europeans	0.0000382
1	europes	0.0000544
2	europes	0.0000244
1	evacuate	0.0000781
2	evacuate	0.0000000
1	evacuated	0.0001448
2	evacuated	0.0000119
1	evacuation	0.0000560
2	evacuation	0.0000154
1	evaluated	0.0000000
2	evaluated	0.0000234
1	evaluation	0.0000232
2	evaluation	0.0000189
1	evaluations	0.0000260
2	evaluations	0.0000052
1	evan	0.0000000
2	evan	0.0000312
1	evangelist	0.0000000
2	evangelist	0.0000390
1	evangelists	0.0000000
2	evangelists	0.0000312
1	evans	0.0000310
2	evans	0.0000134
1	eve	0.0000113
2	eve	0.0000700
1	evening	0.0001811
2	evening	0.0002320
1	evenly	0.0000447
2	evenly	0.0000000
1	event	0.0001587
2	event	0.0001113
1	events	0.0000700
2	events	0.0001693
1	eventual	0.0000000
2	eventual	0.0000468
1	eventually	0.0001908
2	eventually	0.0000928
1	everett	0.0000153
2	everett	0.0000127
1	everybody	0.0001581
2	everybody	0.0001507
1	everybodys	0.0000147
2	everybodys	0.0000170
1	evidence	0.0002159
2	evidence	0.0004765
1	evident	0.0000130
2	evident	0.0000377
1	evil	0.0000000
2	evil	0.0000506
1	evolution	0.0000000
2	evolution	0.0000234
1	evren	0.0000000
2	evren	0.0000234
1	exact	0.0000433
2	exact	0.0000321
1	exactly	0.0000701
2	exactly	0.0001108
1	exam	0.0000091
2	exam	0.0000326
1	examination	0.0000180
2	examination	0.0000614
1	examine	0.0000535
2	examine	0.0000406
1	examined	0.0000151
2	examined	0.0000518
1	examiner	0.0000001
2	examiner	0.0000311
1	examiners	0.0000005
2	examiners	0.0000230
1	examining	0.0000214
2	examining	0.0000435
1	examples	0.0000283
2	examples	0.0000348
1	exams	0.0000010
2	exams	0.0000343
1	exceed	0.0000297
2	exceed	0.0000221
1	exceeded	0.0000234
2	exceeded	0.0000149
1	exceeding	0.0000486
2	exceeding	0.0000050
1	excellent	0.0000441
2	excellent	0.0000121
1	exception	0.0000035
2	exception	0.0000443
1	exceptional	0.0000204
2	exceptional	0.0000130
1	exceptions	0.0000000
2	exceptions	0.0000429
1	excerpts	0.0000000
2	excerpts	0.0000351
1	excess	0.0000335
2	excess	0.0000351
1	excessive	0.0000522
2	excessive	0.0000415
1	exchange	0.0013505
2	exchange	0.0000859
1	exchanged	0.0000006
2	exchanged	0.0000308
1	exchangelisted	0.0001507
2	exchangelisted	0.0000000
1	exchanges	0.0001174
2	exchanges	0.0000622
1	excise	0.0000391
2	excise	0.0000000
1	excited	0.0000279
2	excited	0.0000117
1	excitement	0.0000502
2	excitement	0.0000000
1	exciting	0.0000305
2	exciting	0.0000216
1	exclude	0.0000000
2	exclude	0.0000234
1	excluded	0.0000000
2	excluded	0.0000351
1	excluding	0.0001005
2	excluding	0.0000000
1	exclusive	0.0000303
2	exclusive	0.0000295
1	excuse	0.0000335
2	excuse	0.0000117
1	executed	0.0000101
2	executed	0.0001059
1	execution	0.0000000
2	execution	0.0001130
1	executions	0.0000000
2	executions	0.0000312
1	executive	0.0007354
2	executive	0.0003204
1	executives	0.0003307
2	executives	0.0000068
1	exempt	0.0000232
2	exempt	0.0000227
1	exercise	0.0000984
2	exercise	0.0000793
1	exercised	0.0000100
2	exercised	0.0000242
1	exercises	0.0000138
2	exercises	0.0000332
1	exhibit	0.0000000
2	exhibit	0.0000351
1	exhibits	0.0000053
2	exhibits	0.0000236
1	exhusband	0.0000000
2	exhusband	0.0000273
1	exile	0.0000000
2	exile	0.0001558
1	exiled	0.0000000
2	exiled	0.0000312
1	exiles	0.0000000
2	exiles	0.0000351
1	exist	0.0000516
2	exist	0.0000575
1	existed	0.0000172
2	existed	0.0000426
1	existence	0.0000181
2	existence	0.0000458
1	existing	0.0002248
2	existing	0.0000690
1	exists	0.0000262
2	exists	0.0000596
1	exit	0.0000000
2	exit	0.0000351
1	expand	0.0001186
2	expand	0.0000653
1	expanded	0.0001001
2	expanded	0.0000353
1	expanding	0.0000512
2	expanding	0.0000500
1	expansion	0.0001534
2	expansion	0.0000332
1	expect	0.0002654
2	expect	0.0001615
1	expectation	0.0000245
2	expectation	0.0000141
1	expectations	0.0001603
2	expectations	0.0000284
1	expected	0.0013781
2	expected	0.0006004
1	expecting	0.0000521
2	expecting	0.0000181
1	expects	0.0001674
2	expects	0.0000624
1	expedition	0.0000781
2	expedition	0.0000000
1	expelled	0.0000000
2	expelled	0.0001130
1	expense	0.0000900
2	expense	0.0000346
1	expenses	0.0001152
2	expenses	0.0000637
1	expensive	0.0002919
2	expensive	0.0000183
1	experience	0.0000755
2	experience	0.0001538
1	experienced	0.0000855
2	experienced	0.0000144
1	experiencing	0.0000335
2	experiencing	0.0000000
1	experiment	0.0000278
2	experiment	0.0000430
1	experimental	0.0000347
2	experimental	0.0000069
1	experiments	0.0000837
2	experiments	0.0000000
1	expert	0.0000514
2	expert	0.0000498
1	expertise	0.0000392
2	expertise	0.0000116
1	experts	0.0002509
2	experts	0.0001482
1	expire	0.0000614
2	expire	0.0000000
1	expired	0.0000590
2	expired	0.0000250
1	expires	0.0000701
2	expires	0.0000251
1	explain	0.0000441
2	explain	0.0000705
1	explained	0.0000389
2	explained	0.0000391
1	explaining	0.0000120
2	explaining	0.0000462
1	explains	0.0000310
2	explains	0.0000017
1	explanation	0.0000546
2	explanation	0.0000359
1	explanations	0.0000060
2	explanations	0.0000192
1	exploded	0.0002561
2	exploded	0.0000004
1	exploitation	0.0000001
2	exploitation	0.0000311
1	exploration	0.0000521
2	exploration	0.0000338
1	explore	0.0000501
2	explore	0.0000157
1	explorers	0.0000502
2	explorers	0.0000000
1	exploring	0.0000512
2	exploring	0.0000110
1	explosion	0.0002625
2	explosion	0.0000077
1	explosions	0.0000614
2	explosions	0.0000000
1	explosive	0.0000949
2	explosive	0.0000000
1	explosives	0.0001284
2	explosives	0.0000000
1	export	0.0002440
2	export	0.0000518
1	exporter	0.0000247
2	exporter	0.0000062
1	exporting	0.0000695
2	exporting	0.0000060
1	exports	0.0003592
2	exports	0.0000415
1	exposed	0.0001039
2	exposed	0.0000366
1	exposure	0.0000493
2	exposure	0.0000357
1	express	0.0001512
2	express	0.0000776
1	expressed	0.0000742
2	expressed	0.0002911
1	expressing	0.0000000
2	expressing	0.0000468
1	expression	0.0000000
2	expression	0.0000429
1	extend	0.0000768
2	extend	0.0000282
1	extended	0.0001301
2	extended	0.0000923
1	extending	0.0000938
2	extending	0.0000085
1	extension	0.0000709
2	extension	0.0000323
1	extensive	0.0000499
2	extensive	0.0000431
1	extensively	0.0000335
2	extensively	0.0000000
1	extent	0.0000928
2	extent	0.0000404
1	external	0.0000936
2	external	0.0000165
1	extortion	0.0000000
2	extortion	0.0000545
1	extra	0.0000371
2	extra	0.0000910
1	extradited	0.0000000
2	extradited	0.0000390
1	extradition	0.0000000
2	extradition	0.0001130
1	extraordinary	0.0000000
2	extraordinary	0.0000429
1	extras	0.0000358
2	extras	0.0000023
1	extreme	0.0000375
2	extreme	0.0000440
1	extremely	0.0001256
2	extremely	0.0000643
1	extremists	0.0000776
2	extremists	0.0000354
1	exxon	0.0001228
2	exxon	0.0000000
1	eye	0.0000924
2	eye	0.0000524
1	eyes	0.0000304
2	eyes	0.0001113
1	eyewitnesses	0.0000225
2	eyewitnesses	0.0000077
1	f	0.0001331
2	f	0.0001448
1	fa	0.0000447
2	fa	0.0000000
1	faa	0.0002400
2	faa	0.0000000
1	faber	0.0000000
2	faber	0.0000234
1	fabrics	0.0000000
2	fabrics	0.0000234
1	face	0.0003848
2	face	0.0004833
1	faced	0.0000313
2	faced	0.0001340
1	faces	0.0000099
2	faces	0.0002853
1	facilitate	0.0000069
2	facilitate	0.0000185
1	facilities	0.0003880
2	facilities	0.0000603
1	facility	0.0001737
2	facility	0.0000424
1	facing	0.0000790
2	facing	0.0001318
1	fact	0.0001414
2	fact	0.0003454
1	faction	0.0000000
2	faction	0.0000468
1	factions	0.0000190
2	factions	0.0000296
1	factor	0.0001158
2	factor	0.0000477
1	factories	0.0001355
2	factories	0.0000691
1	factors	0.0001771
2	factors	0.0000166
1	factory	0.0001338
2	factory	0.0000352
1	facts	0.0000004
2	facts	0.0000621
1	faculty	0.0000000
2	faculty	0.0000740
1	fahrenheit	0.0000447
2	fahrenheit	0.0000000
1	fail	0.0000009
2	fail	0.0001240
1	failed	0.0004118
2	failed	0.0003788
1	failing	0.0001041
2	failing	0.0000988
1	fails	0.0000182
2	fails	0.0000418
1	failure	0.0001592
2	failure	0.0001889
1	failures	0.0000719
2	failures	0.0000083
1	fair	0.0005464
2	fair	0.0001757
1	fairly	0.0000456
2	fairly	0.0000422
1	fairness	0.0000000
2	fairness	0.0000662
1	faith	0.0000059
2	faith	0.0001167
1	faithful	0.0000529
2	faithful	0.0000099
1	fakhri	0.0000000
2	fakhri	0.0000312
1	fall	0.0005612
2	fall	0.0002199
1	fallen	0.0001393
2	fallen	0.0000002
1	falling	0.0002385
2	falling	0.0000322
1	falls	0.0001182
2	falls	0.0000656
1	false	0.0000361
2	false	0.0000956
1	fame	0.0000484
2	fame	0.0000363
1	famed	0.0000003
2	famed	0.0000232
1	familiar	0.0000039
2	familiar	0.0000869
1	families	0.0002440
2	families	0.0002700
1	family	0.0003444
2	family	0.0011154
1	familys	0.0000493
2	familys	0.0000903
1	famine	0.0000071
2	famine	0.0000262
1	famous	0.0000926
2	famous	0.0000717
1	fan	0.0000135
2	fan	0.0000218
1	fans	0.0000979
2	fans	0.0000369
1	fantastic	0.0000121
2	fantastic	0.0000188
1	fantasy	0.0000000
2	fantasy	0.0000273
1	far	0.0006568
2	far	0.0005428
1	fare	0.0000650
2	fare	0.0000053
1	fares	0.0000502
2	fares	0.0000000
1	fargo	0.0000379
2	fargo	0.0000047
1	farley	0.0000335
2	farley	0.0000000
1	farm	0.0003754
2	farm	0.0002522
1	farmer	0.0001188
2	farmer	0.0000417
1	farmers	0.0008653
2	farmers	0.0001012
1	farming	0.0000940
2	farming	0.0000240
1	farmland	0.0000893
2	farmland	0.0000000
1	farms	0.0000307
2	farms	0.0000292
1	farreaching	0.0000186
2	farreaching	0.0000182
1	farrell	0.0000243
2	farrell	0.0000454
1	farther	0.0000443
2	farther	0.0000041
1	fashion	0.0000138
2	fashion	0.0000410
1	fast	0.0000793
2	fast	0.0001434
1	faster	0.0000989
2	faster	0.0000284
1	fastest	0.0000502
2	fastest	0.0000000
1	fastfood	0.0000391
2	fastfood	0.0000000
1	fat	0.0000519
2	fat	0.0000105
1	fatal	0.0001585
2	fatal	0.0000530
1	fatalities	0.0000726
2	fatalities	0.0000000
1	fatality	0.0000335
2	fatality	0.0000000
1	fatally	0.0000153
2	fatally	0.0000361
1	fate	0.0000214
2	fate	0.0000786
1	father	0.0000515
2	father	0.0003030
1	fathers	0.0000000
2	fathers	0.0000857
1	fatigue	0.0000502
2	fatigue	0.0000000
1	fault	0.0001035
2	fault	0.0000096
1	faulty	0.0000502
2	faulty	0.0000000
1	favor	0.0000303
2	favor	0.0002204
1	favorable	0.0000860
2	favorable	0.0000452
1	favorably	0.0000186
2	favorably	0.0000143
1	favored	0.0000327
2	favored	0.0001058
1	favorite	0.0000180
2	favorite	0.0001238
1	favors	0.0000110
2	favors	0.0000897
1	fbi	0.0000009
2	fbi	0.0004280
1	fbis	0.0000000
2	fbis	0.0000312
1	fcc	0.0000001
2	fcc	0.0000467
1	fda	0.0002278
2	fda	0.0000046
1	fdic	0.0000512
2	fdic	0.0000071
1	fe	0.0001340
2	fe	0.0000000
1	fear	0.0001320
2	fear	0.0001728
1	feared	0.0000718
2	feared	0.0000823
1	fearing	0.0000270
2	fearing	0.0000084
1	fears	0.0001774
2	fears	0.0000710
1	feature	0.0000212
2	feature	0.0000398
1	featured	0.0000365
2	featured	0.0000330
1	features	0.0000967
2	features	0.0000104
1	featuring	0.0000191
2	featuring	0.0000451
1	feazell	0.0000000
2	feazell	0.0000234
1	feb	0.0001628
2	feb	0.0002682
1	february	0.0004330
2	february	0.0002432
1	fec	0.0000000
2	fec	0.0000390
1	fed	0.0002763
2	fed	0.0000136
1	federal	0.0020462
2	federal	0.0014158
1	federally	0.0000368
2	federally	0.0000211
1	federated	0.0001292
2	federated	0.0000072
1	federation	0.0000917
2	federation	0.0001853
1	federations	0.0000000
2	federations	0.0000390
1	feds	0.0000335
2	feds	0.0000000
1	fee	0.0000924
2	fee	0.0000173
1	feed	0.0001720
2	feed	0.0000085
1	feedback	0.0000611
2	feedback	0.0000080
1	feeder	0.0000558
2	feeder	0.0000000
1	feeding	0.0000346
2	feeding	0.0000148
1	feel	0.0001773
2	feel	0.0003165
1	feeling	0.0000824
2	feeling	0.0000827
1	feelings	0.0000090
2	feelings	0.0000561
1	feels	0.0000404
2	feels	0.0000692
1	feeney	0.0000017
2	feeney	0.0000300
1	fees	0.0001097
2	fees	0.0001767
1	feet	0.0006288
2	feet	0.0000208
1	felix	0.0000000
2	felix	0.0000273
1	fell	0.0014174
2	fell	0.0000002
1	fellow	0.0000149
2	fellow	0.0001532
1	fellows	0.0000168
2	fellows	0.0000155
1	felonies	0.0000002
2	felonies	0.0000310
1	felony	0.0000035
2	felony	0.0000988
1	felt	0.0002182
2	felt	0.0001672
1	female	0.0000490
2	female	0.0000905
1	fence	0.0000480
2	fence	0.0000288
1	ferdinand	0.0000087
2	ferdinand	0.0000446
1	fernandez	0.0000011
2	fernandez	0.0001200
1	fernando	0.0000377
2	fernando	0.0000204
1	ferraro	0.0000000
2	ferraro	0.0000390
1	ferret	0.0000001
2	ferret	0.0000623
1	ferrets	0.0000380
2	ferrets	0.0000163
1	ferry	0.0000299
2	ferry	0.0000103
1	festival	0.0000343
2	festival	0.0000462
1	fetal	0.0000097
2	fetal	0.0000361
1	fetus	0.0000000
2	fetus	0.0000429
1	fever	0.0000193
2	fever	0.0000099
1	fiction	0.0000173
2	fiction	0.0000269
1	fidel	0.0000000
2	fidel	0.0000273
1	field	0.0003665
2	field	0.0001065
1	fields	0.0001673
2	fields	0.0000274
1	fierce	0.0000323
2	fierce	0.0000164
1	fiery	0.0000147
2	fiery	0.0000131
1	fifteen	0.0000351
2	fifteen	0.0000145
1	fifth	0.0001564
2	fifth	0.0001207
1	fight	0.0001627
2	fight	0.0003305
1	fighter	0.0000997
2	fighter	0.0000200
1	fighters	0.0000881
2	fighters	0.0000593
1	fighting	0.0001295
2	fighting	0.0004940
1	fights	0.0000080
2	fights	0.0000373
1	figure	0.0003129
2	figure	0.0001283
1	figures	0.0005244
2	figures	0.0001326
1	fijian	0.0000000
2	fijian	0.0000234
1	fijians	0.0000000
2	fijians	0.0000234
1	file	0.0000805
2	file	0.0001191
1	filed	0.0001877
2	filed	0.0006131
1	files	0.0000309
2	files	0.0000408
1	filibuster	0.0000000
2	filibuster	0.0000351
1	filing	0.0002286
2	filing	0.0000508
1	filipino	0.0000000
2	filipino	0.0000662
1	filipinos	0.0000000
2	filipinos	0.0000312
1	fill	0.0000484
2	fill	0.0000831
1	filled	0.0001098
2	filled	0.0000675
1	filling	0.0000602
2	filling	0.0000125
1	film	0.0001400
2	film	0.0003426
1	filmed	0.0000000
2	filmed	0.0000234
1	filmmakers	0.0000007
2	filmmakers	0.0000268
1	films	0.0000337
2	films	0.0001401
1	final	0.0004095
2	final	0.0004077
1	finalists	0.0000000
2	finalists	0.0000351
1	finance	0.0001889
2	finance	0.0001993
1	financed	0.0000851
2	financed	0.0000185
1	finances	0.0000066
2	finances	0.0000305
1	financial	0.0009617
2	financial	0.0002793
1	financially	0.0000684
2	financially	0.0000107
1	financier	0.0000158
2	financier	0.0000240
1	financing	0.0002783
2	financing	0.0000317
1	finding	0.0000990
2	finding	0.0000868
1	findings	0.0001706
2	findings	0.0000289
1	finds	0.0000425
2	finds	0.0000405
1	fine	0.0000788
2	fine	0.0001826
1	fined	0.0000713
2	fined	0.0000359
1	fines	0.0000532
2	fines	0.0001265
1	finest	0.0000181
2	finest	0.0000146
1	fingerprints	0.0000067
2	fingerprints	0.0000187
1	finish	0.0000788
2	finish	0.0000502
1	finished	0.0002421
2	finished	0.0000492
1	finishing	0.0000306
2	finishing	0.0000059
1	fink	0.0000000
2	fink	0.0000390
1	finland	0.0000186
2	finland	0.0000182
1	finley	0.0000374
2	finley	0.0000051
1	fire	0.0014861
2	fire	0.0001081
1	firearms	0.0000256
2	firearms	0.0000445
1	fired	0.0002059
2	fired	0.0003238
1	firefighters	0.0002791
2	firefighters	0.0000000
1	firefighting	0.0000502
2	firefighting	0.0000000
1	fires	0.0001786
2	fires	0.0000000
1	firing	0.0000315
2	firing	0.0000793
1	firm	0.0006488
2	firm	0.0000926
1	firmly	0.0000211
2	firmly	0.0000398
1	firmness	0.0000330
2	firmness	0.0000003
1	firms	0.0002321
2	firms	0.0000250
1	first	0.0026168
2	first	0.0024239
1	firstclass	0.0000404
2	firstclass	0.0000069
1	firstdegree	0.0000000
2	firstdegree	0.0000935
1	firsthand	0.0000104
2	firsthand	0.0000278
1	firstquarter	0.0000391
2	firstquarter	0.0000000
1	firstterm	0.0000000
2	firstterm	0.0000234
1	firsttime	0.0000267
2	firsttime	0.0000086
1	fis	0.0000000
2	fis	0.0000234
1	fiscal	0.0004172
2	fiscal	0.0001491
1	fish	0.0002967
2	fish	0.0000072
1	fisher	0.0000000
2	fisher	0.0000312
1	fisheries	0.0000447
2	fisheries	0.0000000
1	fishermen	0.0000614
2	fishermen	0.0000000
1	fishing	0.0000994
2	fishing	0.0000475
1	fit	0.0000572
2	fit	0.0000419
1	fitzwater	0.0000142
2	fitzwater	0.0004226
1	five	0.0010871
2	five	0.0010684
1	fiveday	0.0000163
2	fiveday	0.0000315
1	fiveyear	0.0000634
2	fiveyear	0.0000804
1	fix	0.0000404
2	fix	0.0000147
1	fixed	0.0001231
2	fixed	0.0000076
1	fixedrate	0.0000447
2	fixedrate	0.0000000
1	fla	0.0002484
2	fla	0.0000175
1	flag	0.0000086
2	flag	0.0001225
1	flags	0.0000000
2	flags	0.0000545
1	flames	0.0001340
2	flames	0.0000000
1	flared	0.0000190
2	flared	0.0000101
1	flashed	0.0000049
2	flashed	0.0000239
1	flat	0.0000614
2	flat	0.0000000
1	flawed	0.0000236
2	flawed	0.0000147
1	flaws	0.0000254
2	flaws	0.0000095
1	fled	0.0001321
2	fled	0.0001805
1	fledgling	0.0000176
2	fledgling	0.0000150
1	flee	0.0000650
2	flee	0.0000170
1	fleeing	0.0000224
2	fleeing	0.0000350
1	fleet	0.0001451
2	fleet	0.0000000
1	fleisher	0.0000000
2	fleisher	0.0000468
1	flew	0.0001583
2	flew	0.0000765
1	flexibility	0.0000333
2	flexibility	0.0000352
1	flexible	0.0000198
2	flexible	0.0000290
1	flies	0.0000210
2	flies	0.0000243
1	flight	0.0008899
2	flight	0.0000217
1	flights	0.0002940
2	flights	0.0000403
1	fln	0.0000000
2	fln	0.0000273
1	floating	0.0000391
2	floating	0.0000000
1	flock	0.0000332
2	flock	0.0000041
1	flood	0.0001977
2	flood	0.0000101
1	flooded	0.0000502
2	flooded	0.0000000
1	flooding	0.0001619
2	flooding	0.0000000
1	floods	0.0000335
2	floods	0.0000000
1	floor	0.0002133
2	floor	0.0001939
1	floors	0.0000682
2	floors	0.0000147
1	florida	0.0003914
2	florida	0.0001203
1	floridas	0.0000601
2	floridas	0.0000009
1	florio	0.0000558
2	florio	0.0000000
1	flour	0.0000335
2	flour	0.0000000
1	flow	0.0001195
2	flow	0.0000295
1	flower	0.0000336
2	flower	0.0000194
1	flowers	0.0000486
2	flowers	0.0000206
1	flown	0.0002004
2	flown	0.0000004
1	flows	0.0000250
2	flows	0.0000059
1	fluid	0.0000165
2	fluid	0.0000158
1	fly	0.0001604
2	fly	0.0000634
1	flying	0.0002336
2	flying	0.0000551
1	flynn	0.0000117
2	flynn	0.0000347
1	foam	0.0000558
2	foam	0.0000000
1	focus	0.0000753
2	focus	0.0001111
1	focused	0.0000659
2	focused	0.0000787
1	focuses	0.0000247
2	focuses	0.0000140
1	focusing	0.0000423
2	focusing	0.0000172
1	foley	0.0000000
2	foley	0.0000468
1	folk	0.0000000
2	folk	0.0000351
1	folks	0.0000279
2	folks	0.0000234
1	follow	0.0000915
2	follow	0.0001348
1	followed	0.0004100
2	followed	0.0002437
1	followers	0.0000002
2	followers	0.0000583
1	follows	0.0000407
2	follows	0.0000300
1	followup	0.0000511
2	followup	0.0000189
1	food	0.0010885
2	food	0.0002337
1	foods	0.0001730
2	foods	0.0000000
1	foot	0.0003410
2	foot	0.0000425
1	footage	0.0000357
2	footage	0.0000218
1	football	0.0000598
2	football	0.0000634
1	foothigh	0.0000216
2	foothigh	0.0000083
1	forbidden	0.0000090
2	forbidden	0.0000210
1	forbids	0.0000038
2	forbids	0.0000324
1	force	0.0007983
2	force	0.0010089
1	forced	0.0003437
2	forced	0.0003289
1	forces	0.0001050
2	forces	0.0011033
1	forcing	0.0000841
2	forcing	0.0000465
1	ford	0.0002844
2	ford	0.0000625
1	fords	0.0000179
2	fords	0.0000148
1	forecast	0.0003795
2	forecast	0.0000000
1	forecaster	0.0000335
2	forecaster	0.0000000
1	forecasters	0.0000837
2	forecasters	0.0000000
1	forecasting	0.0000391
2	forecasting	0.0000000
1	forecasts	0.0000837
2	forecasts	0.0000039
1	foreclosure	0.0000000
2	foreclosure	0.0000234
1	foreign	0.0005879
2	foreign	0.0014441
1	foreigners	0.0000762
2	foreigners	0.0001845
1	foremost	0.0000159
2	foremost	0.0000123
1	forensic	0.0000032
2	forensic	0.0000211
1	foresee	0.0000173
2	foresee	0.0000113
1	forest	0.0002633
2	forest	0.0000305
1	forestry	0.0000139
2	forestry	0.0000292
1	forests	0.0000884
2	forests	0.0000006
1	forever	0.0000031
2	forever	0.0000290
1	forge	0.0000000
2	forge	0.0000234
1	forget	0.0000079
2	forget	0.0000490
1	forging	0.0000195
2	forging	0.0000098
1	forgotten	0.0000002
2	forgotten	0.0000388
1	forks	0.0000558
2	forks	0.0000000
1	form	0.0001448
2	form	0.0003470
1	formal	0.0000295
2	formal	0.0001781
1	formally	0.0000210
2	formally	0.0000944
1	forman	0.0000000
2	forman	0.0000234
1	format	0.0000206
2	format	0.0000129
1	formation	0.0000120
2	formation	0.0000618
1	formed	0.0001745
2	formed	0.0001431
1	forming	0.0000132
2	forming	0.0000336
1	forms	0.0000899
2	forms	0.0001321
1	formula	0.0000447
2	formula	0.0000000
1	fort	0.0001951
2	fort	0.0000703
1	forth	0.0000316
2	forth	0.0000637
1	forthcoming	0.0000147
2	forthcoming	0.0000209
1	fortified	0.0000119
2	fortified	0.0000229
1	fortune	0.0000192
2	fortune	0.0000256
1	forum	0.0000000
2	forum	0.0000468
1	forward	0.0001205
2	forward	0.0002509
1	foster	0.0000000
2	foster	0.0000429
1	fought	0.0000309
2	fought	0.0001577
1	found	0.0013097
2	found	0.0008351
1	foundation	0.0001383
2	foundation	0.0001216
1	foundations	0.0000201
2	foundations	0.0000132
1	founded	0.0000904
2	founded	0.0001161
1	founder	0.0000315
2	founder	0.0000754
1	founding	0.0000000
2	founding	0.0000312
1	four	0.0010268
2	four	0.0012468
1	fourday	0.0000070
2	fourday	0.0000535
1	fournier	0.0000000
2	fournier	0.0000234
1	fourth	0.0002213
2	fourth	0.0001845
1	fourthquarter	0.0000670
2	fourthquarter	0.0000000
1	fouryear	0.0000284
2	fouryear	0.0000231
1	fox	0.0000669
2	fox	0.0000351
1	fractures	0.0000335
2	fractures	0.0000000
1	fragile	0.0000079
2	fragile	0.0000218
1	frail	0.0000000
2	frail	0.0000273
1	frame	0.0000133
2	frame	0.0000180
1	framed	0.0000094
2	framed	0.0000207
1	framework	0.0000000
2	framework	0.0000468
1	franc	0.0000892
2	franc	0.0000000
1	france	0.0002351
2	france	0.0002878
1	frances	0.0000616
2	frances	0.0000310
1	francis	0.0000775
2	francis	0.0000277
1	francisco	0.0004295
2	francisco	0.0000898
1	franciscobased	0.0000134
2	franciscobased	0.0000218
1	francois	0.0000056
2	francois	0.0000506
1	francs	0.0003126
2	francs	0.0000000
1	frank	0.0001325
2	frank	0.0002854
1	frankfurt	0.0001074
2	frankfurt	0.0000107
1	franklin	0.0000271
2	franklin	0.0000512
1	frankly	0.0000000
2	frankly	0.0000351
1	fraud	0.0000365
2	fraud	0.0003213
1	fred	0.0001133
2	fred	0.0000495
1	frederick	0.0000409
2	frederick	0.0000221
1	free	0.0001701
2	free	0.0006643
1	freed	0.0000410
2	freed	0.0002129
1	freedom	0.0000090
2	freedom	0.0003755
1	freedoms	0.0000000
2	freedoms	0.0000545
1	freeing	0.0000000
2	freeing	0.0000312
1	freelance	0.0000381
2	freelance	0.0000007
1	freely	0.0000000
2	freely	0.0000429
1	freemarket	0.0000068
2	freemarket	0.0000264
1	freeze	0.0000145
2	freeze	0.0000795
1	freezing	0.0000390
2	freezing	0.0000039
1	freight	0.0000502
2	freight	0.0000000
1	freighter	0.0000502
2	freighter	0.0000000
1	french	0.0003655
2	french	0.0003020
1	frequent	0.0000076
2	frequent	0.0000609
1	frequently	0.0000351
2	frequently	0.0001080
1	fresh	0.0001754
2	fresh	0.0000295
1	fresno	0.0000447
2	fresno	0.0000000
1	friction	0.0000000
2	friction	0.0000234
1	friday	0.0017428
2	friday	0.0013276
1	fridays	0.0002170
2	fridays	0.0000472
1	fried	0.0000206
2	fried	0.0000090
1	friedman	0.0000110
2	friedman	0.0000157
1	friedrick	0.0000000
2	friedrick	0.0000390
1	friend	0.0000244
2	friend	0.0002051
1	friendly	0.0000515
2	friendly	0.0000576
1	friends	0.0001443
2	friends	0.0003161
1	friendship	0.0000000
2	friendship	0.0000545
1	frigate	0.0000515
2	frigate	0.0000108
1	frohnmayer	0.0000000
2	frohnmayer	0.0000390
1	front	0.0002785
2	front	0.0005185
1	frontier	0.0000138
2	frontier	0.0000137
1	frontrunner	0.0000000
2	frontrunner	0.0000584
1	fronts	0.0000000
2	fronts	0.0000312
1	frozen	0.0001327
2	frozen	0.0000126
1	fruehauf	0.0000335
2	fruehauf	0.0000000
1	fruit	0.0001319
2	fruit	0.0000093
1	fruits	0.0000427
2	fruits	0.0000130
1	frustrated	0.0000103
2	frustrated	0.0000396
1	frustration	0.0000000
2	frustration	0.0000429
1	fuel	0.0004236
2	fuel	0.0000199
1	fueled	0.0000333
2	fueled	0.0000196
1	fueling	0.0000390
2	fueling	0.0000000
1	fuels	0.0000557
2	fuels	0.0000001
1	fugitive	0.0000012
2	fugitive	0.0000265
1	fuji	0.0000041
2	fuji	0.0000205
1	fujimori	0.0000000
2	fujimori	0.0000974
1	fulfill	0.0000012
2	fulfill	0.0000498
1	full	0.0002342
2	full	0.0004482
1	fuller	0.0000001
2	fuller	0.0000350
1	fulltime	0.0000771
2	fulltime	0.0000007
1	fully	0.0001377
2	fully	0.0001259
1	fun	0.0000527
2	fun	0.0000606
1	function	0.0000471
2	function	0.0000333
1	functioning	0.0000457
2	functioning	0.0000226
1	fund	0.0003218
2	fund	0.0002546
1	fundamental	0.0000313
2	fundamental	0.0000716
1	fundamentalist	0.0000000
2	fundamentalist	0.0000779
1	fundamentalists	0.0000000
2	fundamentalists	0.0000584
1	fundamentally	0.0000000
2	fundamentally	0.0000351
1	funded	0.0000115
2	funded	0.0000271
1	funding	0.0000105
2	funding	0.0000745
1	fundraising	0.0000000
2	fundraising	0.0000857
1	funds	0.0003704
2	funds	0.0002713
1	funeral	0.0000685
2	funeral	0.0000730
1	furlough	0.0000000
2	furlough	0.0000390
1	furloughs	0.0000000
2	furloughs	0.0000429
1	furmark	0.0000015
2	furmark	0.0000262
1	furniture	0.0000658
2	furniture	0.0000047
1	future	0.0003370
2	future	0.0005011
1	futures	0.0009377
2	futures	0.0000000
1	fw	0.0000000
2	fw	0.0000312
1	g	0.0001180
2	g	0.0000462
1	ga	0.0000295
2	ga	0.0000262
1	gacy	0.0000670
2	gacy	0.0000000
1	gadhafi	0.0000000
2	gadhafi	0.0000312
1	gain	0.0003357
2	gain	0.0000812
1	gained	0.0003406
2	gained	0.0000545
1	gainers	0.0001228
2	gainers	0.0000000
1	gaining	0.0000736
2	gaining	0.0000694
1	gains	0.0003704
2	gains	0.0000259
1	gairy	0.0000000
2	gairy	0.0000234
1	galileo	0.0000837
2	galileo	0.0000000
1	galileos	0.0000335
2	galileos	0.0000000
1	gallery	0.0000476
2	gallery	0.0000213
1	gallo	0.0000557
2	gallo	0.0000040
1	gallon	0.0002847
2	gallon	0.0000000
1	gallons	0.0001395
2	gallons	0.0000000
1	galveston	0.0000558
2	galveston	0.0000000
1	gamble	0.0000529
2	gamble	0.0000176
1	gambling	0.0000753
2	gambling	0.0000098
1	game	0.0000899
2	game	0.0002100
1	games	0.0001389
2	games	0.0001407
1	gandhi	0.0000000
2	gandhi	0.0000312
1	gang	0.0001008
2	gang	0.0000621
1	gangs	0.0000618
2	gangs	0.0000192
1	gans	0.0000000
2	gans	0.0000234
1	ganyile	0.0000000
2	ganyile	0.0000351
1	gao	0.0000708
2	gao	0.0000129
1	gap	0.0000339
2	gap	0.0000153
1	garage	0.0000418
2	garage	0.0000098
1	garbage	0.0001204
2	garbage	0.0000874
1	garcia	0.0000000
2	garcia	0.0001247
1	garden	0.0001302
2	garden	0.0000455
1	gardeners	0.0000335
2	gardeners	0.0000000
1	gardens	0.0000276
2	gardens	0.0000080
1	gardner	0.0000335
2	gardner	0.0000000
1	garland	0.0000333
2	garland	0.0000001
1	garrett	0.0000500
2	garrett	0.0000001
1	garrison	0.0000330
2	garrison	0.0000121
1	garrity	0.0000000
2	garrity	0.0000234
1	garry	0.0000001
2	garry	0.0000233
1	gary	0.0000576
2	gary	0.0000805
1	gas	0.0004563
2	gas	0.0001373
1	gases	0.0000502
2	gases	0.0000000
1	gasoline	0.0004968
2	gasoline	0.0000000
1	gate	0.0000481
2	gate	0.0000366
1	gates	0.0000946
2	gates	0.0000002
1	gather	0.0000000
2	gather	0.0000701
1	gathered	0.0000460
2	gathered	0.0001821
1	gathering	0.0000144
2	gathering	0.0000951
1	gatt	0.0000239
2	gatt	0.0000534
1	gauge	0.0000800
2	gauge	0.0000026
1	gave	0.0003038
2	gave	0.0005944
1	gaviria	0.0000000
2	gaviria	0.0000234
1	gaza	0.0000000
2	gaza	0.0001987
1	gdansk	0.0000000
2	gdansk	0.0000351
1	gdynia	0.0000217
2	gdynia	0.0000082
1	ge	0.0000726
2	ge	0.0000000
1	geagea	0.0000447
2	geagea	0.0000000
1	geageas	0.0000502
2	geageas	0.0000000
1	gear	0.0000571
2	gear	0.0000030
1	gebelwilliams	0.0000391
2	gebelwilliams	0.0000000
1	gen	0.0000030
2	gen	0.0003447
1	gene	0.0001467
2	gene	0.0000145
1	general	0.0009485
2	general	0.0011729
1	generale	0.0000327
2	generale	0.0000006
1	generals	0.0000275
2	generals	0.0001444
1	generate	0.0000574
2	generate	0.0000145
1	generated	0.0000828
2	generated	0.0000240
1	generating	0.0000614
2	generating	0.0000000
1	generation	0.0000420
2	generation	0.0000291
1	generations	0.0000106
2	generations	0.0000160
1	genes	0.0000460
2	genes	0.0000029
1	genetic	0.0000239
2	genetic	0.0000106
1	genetically	0.0000447
2	genetically	0.0000000
1	geneva	0.0000026
2	geneva	0.0001618
1	genocide	0.0000000
2	genocide	0.0000273
1	genscher	0.0000000
2	genscher	0.0000506
1	genuine	0.0000000
2	genuine	0.0000545
1	geographical	0.0000138
2	geographical	0.0000177
1	geological	0.0000893
2	geological	0.0000000
1	george	0.0001098
2	george	0.0008428
1	georges	0.0000170
2	georges	0.0000115
1	georgia	0.0001172
2	georgia	0.0000857
1	gephardt	0.0000000
2	gephardt	0.0001753
1	gerald	0.0000612
2	gerald	0.0000235
1	gerard	0.0000258
2	gerard	0.0000209
1	gerhard	0.0000000
2	gerhard	0.0000273
1	german	0.0004307
2	german	0.0007630
1	germans	0.0000274
2	germans	0.0001601
1	germany	0.0001946
2	germany	0.0008265
1	germanys	0.0000202
2	germanys	0.0002002
1	gesell	0.0000000
2	gesell	0.0001130
1	gesture	0.0000000
2	gesture	0.0000662
1	get	0.0011849
2	get	0.0012066
1	gets	0.0000650
2	gets	0.0001767
1	getting	0.0003889
2	getting	0.0002545
1	getz	0.0000000
2	getz	0.0000429
1	giant	0.0002189
2	giant	0.0000303
1	gibbons	0.0000000
2	gibbons	0.0000234
1	gibbs	0.0000335
2	gibbs	0.0000000
1	gibson	0.0000558
2	gibson	0.0000000
1	gift	0.0000000
2	gift	0.0000584
1	gifts	0.0000000
2	gifts	0.0000662
1	gilbert	0.0000932
2	gilbert	0.0000206
1	gill	0.0000000
2	gill	0.0000312
1	gillespie	0.0000047
2	gillespie	0.0000201
1	gingrich	0.0000000
2	gingrich	0.0000351
1	girl	0.0000615
2	girl	0.0002064
1	girlfriend	0.0000012
2	girlfriend	0.0000225
1	girls	0.0000815
2	girls	0.0000951
1	gisclair	0.0000614
2	gisclair	0.0000000
1	give	0.0003427
2	give	0.0008789
1	gives	0.0000507
2	gives	0.0001750
1	giving	0.0000894
2	giving	0.0002921
1	glad	0.0000000
2	glad	0.0000584
1	glantz	0.0000335
2	glantz	0.0000000
1	glasnost	0.0000000
2	glasnost	0.0000857
1	glass	0.0001243
2	glass	0.0000146
1	glasses	0.0000000
2	glasses	0.0000312
1	glauberman	0.0000000
2	glauberman	0.0000273
1	glenn	0.0000361
2	glenn	0.0000683
1	glimpse	0.0000090
2	glimpse	0.0000171
1	global	0.0001728
2	global	0.0000781
1	globe	0.0000296
2	globe	0.0000456
1	gloomy	0.0000187
2	gloomy	0.0000142
1	glory	0.0000000
2	glory	0.0000429
1	glow	0.0000242
2	glow	0.0000065
1	gm	0.0001563
2	gm	0.0000000
1	gms	0.0000391
2	gms	0.0000000
1	gnp	0.0001451
2	gnp	0.0000000
1	go	0.0007476
2	go	0.0010950
1	goal	0.0000789
2	goal	0.0001280
1	goals	0.0000643
2	goals	0.0000759
1	god	0.0000278
2	god	0.0001832
1	godfather	0.0000335
2	godfather	0.0000000
1	gods	0.0000085
2	gods	0.0000486
1	goes	0.0001305
2	goes	0.0001894
1	going	0.0009544
2	going	0.0013948
1	gold	0.0009600
2	gold	0.0000000
1	goldberg	0.0000802
2	goldberg	0.0000141
1	golden	0.0001311
2	golden	0.0000098
1	goldman	0.0000670
2	goldman	0.0000000
1	golf	0.0000526
2	golf	0.0000373
1	gomes	0.0000260
2	gomes	0.0000130
1	gone	0.0001436
2	gone	0.0001958
1	gonzalez	0.0000000
2	gonzalez	0.0000312
1	good	0.0008123
2	good	0.0009602
1	goodbye	0.0000223
2	goodbye	0.0000117
1	goodman	0.0000000
2	goodman	0.0000584
1	goods	0.0005423
2	goods	0.0000305
1	gop	0.0000000
2	gop	0.0003273
1	gorbachev	0.0000000
2	gorbachev	0.0012233
1	gorbachevs	0.0000000
2	gorbachevs	0.0003896
1	gordon	0.0000264
2	gordon	0.0000751
1	gore	0.0000000
2	gore	0.0001636
1	got	0.0007240
2	got	0.0006206
1	gotner	0.0000558
2	gotner	0.0000000
1	gotten	0.0000896
2	gotten	0.0000622
1	gotti	0.0000000
2	gotti	0.0000584
1	gourmet	0.0000332
2	gourmet	0.0000002
1	gov	0.0000170
2	gov	0.0005024
1	governed	0.0000000
2	governed	0.0000351
1	governing	0.0000000
2	governing	0.0001403
1	government	0.0014115
2	government	0.0045198
1	governmental	0.0000058
2	governmental	0.0000388
1	governmentowned	0.0000614
2	governmentowned	0.0000000
1	governments	0.0002100
2	governments	0.0004300
1	governor	0.0000421
2	governor	0.0004888
1	governors	0.0000000
2	governors	0.0001753
1	gown	0.0000005
2	gown	0.0000386
1	grabbed	0.0000700
2	grabbed	0.0000018
1	grace	0.0000000
2	grace	0.0000312
1	gracyalny	0.0000310
2	gracyalny	0.0000017
1	grade	0.0001000
2	grade	0.0000042
1	gradual	0.0000060
2	gradual	0.0000192
1	gradually	0.0000340
2	gradually	0.0000464
1	graduate	0.0000312
2	graduate	0.0000834
1	graduated	0.0000020
2	graduated	0.0000454
1	graduates	0.0000903
2	graduates	0.0000188
1	graduation	0.0000126
2	graduation	0.0000302
1	graham	0.0000315
2	graham	0.0000325
1	grain	0.0003349
2	grain	0.0000000
1	grains	0.0000558
2	grains	0.0000000
1	gramm	0.0000000
2	gramm	0.0000468
1	grammer	0.0000000
2	grammer	0.0000545
1	grammrudman	0.0000466
2	grammrudman	0.0000376
1	grand	0.0001137
2	grand	0.0003025
1	grandchildren	0.0000083
2	grandchildren	0.0000293
1	grande	0.0000670
2	grande	0.0000000
1	grandfather	0.0000211
2	grandfather	0.0000087
1	grandmother	0.0000242
2	grandmother	0.0000337
1	grandmothers	0.0000020
2	grandmothers	0.0000220
1	grandson	0.0000105
2	grandson	0.0000200
1	grant	0.0000432
2	grant	0.0001490
1	granted	0.0000136
2	granted	0.0001814
1	granting	0.0000094
2	granting	0.0000597
1	grants	0.0000161
2	grants	0.0001290
1	grape	0.0000391
2	grape	0.0000000
1	grapes	0.0000174
2	grapes	0.0000502
1	grass	0.0000447
2	grass	0.0000000
1	grassgreen	0.0000159
2	grassgreen	0.0000239
1	grassley	0.0000000
2	grassley	0.0000545
1	grassroots	0.0000007
2	grassroots	0.0000229
1	grateful	0.0000419
2	grateful	0.0000175
1	grave	0.0000000
2	grave	0.0000935
1	graves	0.0000001
2	graves	0.0000428
1	gravity	0.0000298
2	gravity	0.0000026
1	gravley	0.0000446
2	gravley	0.0000000
1	gray	0.0000228
2	gray	0.0001360
1	great	0.0004153
2	great	0.0004971
1	greater	0.0001600
2	greater	0.0001961
1	greatest	0.0000886
2	greatest	0.0000628
1	greatly	0.0000247
2	greatly	0.0000256
1	greece	0.0000302
2	greece	0.0000451
1	greed	0.0000000
2	greed	0.0000467
1	greedy	0.0000141
2	greedy	0.0000175
1	greek	0.0000309
2	greek	0.0000681
1	green	0.0000839
2	green	0.0001051
1	greene	0.0000000
2	greene	0.0000234
1	greenhouse	0.0000702
2	greenhouse	0.0000055
1	greenpeace	0.0000520
2	greenpeace	0.0000065
1	greenspan	0.0000949
2	greenspan	0.0000000
1	greenwald	0.0000614
2	greenwald	0.0000000
1	greeted	0.0000126
2	greeted	0.0000535
1	greg	0.0000271
2	greg	0.0000317
1	gregg	0.0000000
2	gregg	0.0000584
1	gregory	0.0000358
2	gregory	0.0000568
1	grenada	0.0000209
2	grenada	0.0000478
1	grenade	0.0000315
2	grenade	0.0000014
1	grenades	0.0000558
2	grenades	0.0000000
1	grew	0.0002152
2	grew	0.0000290
1	greyhound	0.0001953
2	greyhound	0.0000000
1	grievances	0.0000447
2	grievances	0.0000000
1	grigoryants	0.0000000
2	grigoryants	0.0001208
1	grimm	0.0000335
2	grimm	0.0000000
1	grip	0.0000347
2	grip	0.0000304
1	grocery	0.0000541
2	grocery	0.0000246
1	gross	0.0001219
2	gross	0.0000279
1	ground	0.0005499
2	ground	0.0001460
1	grounds	0.0000342
2	grounds	0.0001437
1	group	0.0011955
2	group	0.0015615
1	groups	0.0002296
2	groups	0.0008176
1	grow	0.0001694
2	grow	0.0000376
1	grower	0.0000335
2	grower	0.0000000
1	growers	0.0001005
2	growers	0.0000000
1	growing	0.0002891
2	growing	0.0001761
1	grown	0.0001460
2	grown	0.0000228
1	grows	0.0000272
2	grows	0.0000122
1	growth	0.0006831
2	growth	0.0000297
1	gruber	0.0000335
2	gruber	0.0000000
1	grumman	0.0001005
2	grumman	0.0000000
1	gte	0.0000575
2	gte	0.0000066
1	guarantee	0.0000335
2	guarantee	0.0001091
1	guaranteed	0.0000145
2	guaranteed	0.0000444
1	guarantees	0.0000117
2	guarantees	0.0000970
1	guard	0.0003649
2	guard	0.0001349
1	guarded	0.0000440
2	guarded	0.0000317
1	guardian	0.0000000
2	guardian	0.0000312
1	guardians	0.0000000
2	guardians	0.0000234
1	guarding	0.0000006
2	guarding	0.0000308
1	guards	0.0000347
2	guards	0.0001511
1	guatemala	0.0000072
2	guatemala	0.0000417
1	gubernatorial	0.0000000
2	gubernatorial	0.0000740
1	guerrilla	0.0000003
2	guerrilla	0.0001595
1	guerrillas	0.0000046
2	guerrillas	0.0002967
1	guess	0.0000662
2	guess	0.0000707
1	guest	0.0000002
2	guest	0.0000505
1	guests	0.0000297
2	guests	0.0000805
1	guida	0.0000000
2	guida	0.0000429
1	guidance	0.0000411
2	guidance	0.0000103
1	guide	0.0000906
2	guide	0.0000069
1	guided	0.0000003
2	guided	0.0000348
1	guideline	0.0000000
2	guideline	0.0000234
1	guidelines	0.0000292
2	guidelines	0.0000926
1	guild	0.0000222
2	guild	0.0000274
1	guilders	0.0001116
2	guilders	0.0000000
1	guilty	0.0000001
2	guilty	0.0005181
1	guinness	0.0000502
2	guinness	0.0000000
1	guitar	0.0000328
2	guitar	0.0000083
1	gulf	0.0003750
2	gulf	0.0003733
1	gull	0.0000502
2	gull	0.0000000
1	gun	0.0001359
2	gun	0.0001428
1	gunfire	0.0000413
2	gunfire	0.0000335
1	gunman	0.0001465
2	gunman	0.0000029
1	gunmen	0.0002177
2	gunmen	0.0000000
1	gunned	0.0000000
2	gunned	0.0000312
1	gunpoint	0.0000000
2	gunpoint	0.0000273
1	guns	0.0001268
2	guns	0.0001180
1	gunshot	0.0000382
2	gunshot	0.0000357
1	gunter	0.0000558
2	gunter	0.0000000
1	gursky	0.0000335
2	gursky	0.0000000
1	gusts	0.0000335
2	gusts	0.0000000
1	guterman	0.0000447
2	guterman	0.0000000
1	guthrie	0.0000447
2	guthrie	0.0000000
1	gutierrez	0.0000346
2	gutierrez	0.0000070
1	guy	0.0000725
2	guy	0.0001053
1	guys	0.0001074
2	guys	0.0000303
1	h	0.0000896
2	h	0.0001868
1	habit	0.0000089
2	habit	0.0000172
1	habitat	0.0000707
2	habitat	0.0000052
1	habits	0.0000192
2	habits	0.0000100
1	hacker	0.0000335
2	hacker	0.0000000
1	hadnt	0.0000563
2	hadnt	0.0000348
1	hadson	0.0000502
2	hadson	0.0000000
1	hafez	0.0000082
2	hafez	0.0000177
1	hahn	0.0000000
2	hahn	0.0000234
1	hail	0.0000670
2	hail	0.0000000
1	hailed	0.0000102
2	hailed	0.0001253
1	hair	0.0000279
2	hair	0.0001753
1	haiti	0.0000000
2	haiti	0.0001480
1	haitian	0.0000000
2	haitian	0.0000468
1	haitis	0.0000000
2	haitis	0.0000545
1	hakim	0.0000000
2	hakim	0.0000312
1	haldeman	0.0000000
2	haldeman	0.0000390
1	hale	0.0000855
2	hale	0.0000260
1	half	0.0008654
2	half	0.0002141
1	halfdozen	0.0000249
2	halfdozen	0.0000333
1	halfhour	0.0000385
2	halfhour	0.0000160
1	halfway	0.0000000
2	halfway	0.0000312
1	hall	0.0000086
2	hall	0.0002005
1	halloween	0.0000781
2	halloween	0.0000000
1	hallway	0.0000161
2	hallway	0.0000161
1	hallways	0.0000335
2	hallways	0.0000000
1	halt	0.0000845
2	halt	0.0000579
1	halted	0.0000331
2	halted	0.0000626
1	halting	0.0000160
2	halting	0.0000200
1	hamadi	0.0000391
2	hamadi	0.0000000
1	hamel	0.0000447
2	hamel	0.0000000
1	hamilton	0.0000576
2	hamilton	0.0000650
1	hammer	0.0000195
2	hammer	0.0000097
1	hampden	0.0000370
2	hampden	0.0000210
1	hampered	0.0000374
2	hampered	0.0000011
1	hampshire	0.0000006
2	hampshire	0.0001749
1	hand	0.0001193
2	hand	0.0002712
1	handed	0.0000253
2	handed	0.0000681
1	handful	0.0000031
2	handful	0.0000836
1	handgun	0.0000028
2	handgun	0.0000526
1	handguns	0.0000000
2	handguns	0.0000234
1	handicap	0.0000019
2	handicap	0.0000221
1	handicapped	0.0000258
2	handicapped	0.0000521
1	handing	0.0000031
2	handing	0.0000212
1	handle	0.0001310
2	handle	0.0000722
1	handled	0.0000470
2	handled	0.0000412
1	handlers	0.0000069
2	handlers	0.0000263
1	handles	0.0000285
2	handles	0.0000191
1	handling	0.0000943
2	handling	0.0000978
1	hands	0.0001241
2	hands	0.0002328
1	hanford	0.0000477
2	hanford	0.0000134
1	hang	0.0000142
2	hang	0.0000446
1	hangar	0.0000391
2	hangar	0.0000000
1	hanged	0.0000000
2	hanged	0.0000273
1	hanging	0.0000181
2	hanging	0.0000302
1	hanover	0.0000271
2	hanover	0.0000044
1	hans	0.0000000
2	hans	0.0000273
1	hansdietrich	0.0000000
2	hansdietrich	0.0000234
1	happen	0.0000885
2	happen	0.0001603
1	happened	0.0001437
2	happened	0.0001841
1	happening	0.0000292
2	happening	0.0000381
1	happens	0.0000459
2	happens	0.0000732
1	happy	0.0000260
2	happy	0.0001026
1	harassed	0.0000079
2	harassed	0.0000257
1	harassment	0.0000000
2	harassment	0.0001091
1	harbor	0.0000781
2	harbor	0.0000000
1	hard	0.0002086
2	hard	0.0003141
1	harder	0.0000187
2	harder	0.0000376
1	hardest	0.0000670
2	hardest	0.0000000
1	hardline	0.0000000
2	hardline	0.0000701
1	hardliners	0.0000000
2	hardliners	0.0000545
1	hardly	0.0000320
2	hardly	0.0000322
1	hardware	0.0000614
2	hardware	0.0000000
1	harlem	0.0000000
2	harlem	0.0000312
1	harm	0.0000275
2	harm	0.0000587
1	harmed	0.0000249
2	harmed	0.0000060
1	harmful	0.0000487
2	harmful	0.0000011
1	harmon	0.0000002
2	harmon	0.0000310
1	harmony	0.0000075
2	harmony	0.0000181
1	harold	0.0000460
2	harold	0.0000536
1	harris	0.0001478
2	harris	0.0000099
1	harrison	0.0000060
2	harrison	0.0000308
1	harry	0.0000220
2	harry	0.0000820
1	harsh	0.0000051
2	harsh	0.0000588
1	harshly	0.0000000
2	harshly	0.0000273
1	hart	0.0000000
2	hart	0.0000273
1	hartford	0.0000291
2	hartford	0.0000070
1	harvard	0.0000743
2	harvard	0.0000689
1	harvest	0.0001674
2	harvest	0.0000000
1	harvested	0.0000332
2	harvested	0.0000002
1	harvests	0.0000335
2	harvests	0.0000000
1	harvey	0.0000279
2	harvey	0.0000195
1	harwood	0.0000335
2	harwood	0.0000000
1	hasegawa	0.0000000
2	hasegawa	0.0000234
1	hasnt	0.0001314
2	hasnt	0.0000641
1	hasselbring	0.0000447
2	hasselbring	0.0000000
1	hastily	0.0000167
2	hastily	0.0000117
1	hastings	0.0000000
2	hastings	0.0000351
1	hat	0.0000341
2	hat	0.0000074
1	hatch	0.0000170
2	hatch	0.0000271
1	hate	0.0000173
2	hate	0.0000269
1	hated	0.0000000
2	hated	0.0000312
1	hatred	0.0000002
2	hatred	0.0000310
1	haul	0.0000242
2	haul	0.0000104
1	hauled	0.0000272
2	hauled	0.0000044
1	havana	0.0000105
2	havana	0.0000550
1	havel	0.0000000
2	havel	0.0000857
1	haven	0.0000715
2	haven	0.0000241
1	havent	0.0001018
2	havent	0.0001198
1	hawaii	0.0000753
2	hawaii	0.0000410
1	hay	0.0000726
2	hay	0.0000000
1	hazardous	0.0001116
2	hazardous	0.0000000
1	hazards	0.0000501
2	hazards	0.0000001
1	hazelwood	0.0000335
2	hazelwood	0.0000000
1	head	0.0004792
2	head	0.0006512
1	headed	0.0001941
2	headed	0.0001528
1	heading	0.0000425
2	heading	0.0000483
1	headlines	0.0000297
2	headlines	0.0000182
1	headquartered	0.0000286
2	headquartered	0.0000073
1	headquarters	0.0001271
2	headquarters	0.0003086
1	heads	0.0000490
2	heads	0.0002074
1	healing	0.0000000
2	healing	0.0000312
1	health	0.0007907
2	health	0.0004844
1	healthy	0.0000684
2	healthy	0.0000224
1	hear	0.0000549
2	hear	0.0002383
1	heard	0.0001651
2	heard	0.0002237
1	hearing	0.0000912
2	hearing	0.0005363
1	hearings	0.0000727
2	hearings	0.0001557
1	heart	0.0000002
2	heart	0.0004985
1	hearts	0.0000201
2	hearts	0.0000483
1	heat	0.0003871
2	heat	0.0000493
1	heath	0.0000000
2	heath	0.0000234
1	heating	0.0002177
2	heating	0.0000000
1	heavier	0.0000558
2	heavier	0.0000000
1	heaviest	0.0000447
2	heaviest	0.0000000
1	heavily	0.0001537
2	heavily	0.0001070
1	heavy	0.0006130
2	heavy	0.0000357
1	hebrew	0.0000000
2	hebrew	0.0000273
1	hed	0.0000002
2	hed	0.0000895
1	hedges	0.0000000
2	hedges	0.0000273
1	heels	0.0000000
2	heels	0.0000273
1	heflin	0.0000000
2	heflin	0.0000273
1	height	0.0000351
2	height	0.0000261
1	heightened	0.0000220
2	heightened	0.0000158
1	heights	0.0000101
2	heights	0.0000436
1	held	0.0004883
2	held	0.0009838
1	helen	0.0000005
2	helen	0.0000269
1	helicopter	0.0002303
2	helicopter	0.0000419
1	helicopters	0.0001457
2	helicopters	0.0000424
1	hell	0.0000361
2	hell	0.0000994
1	helms	0.0000000
2	helms	0.0000623
1	helmut	0.0000057
2	helmut	0.0000544
1	help	0.0008120
2	help	0.0010501
1	helped	0.0003328
2	helped	0.0002118
1	helpful	0.0000000
2	helpful	0.0000429
1	helping	0.0000505
2	helping	0.0001167
1	helps	0.0000698
2	helps	0.0000175
1	helsinki	0.0000081
2	helsinki	0.0000216
1	hemisphere	0.0000228
2	hemisphere	0.0000113
1	henri	0.0000107
2	henri	0.0000159
1	henry	0.0001078
2	henry	0.0001780
1	henson	0.0000837
2	henson	0.0000000
1	hensons	0.0000391
2	hensons	0.0000000
1	hepatitis	0.0000523
2	hepatitis	0.0000063
1	herald	0.0000000
2	herald	0.0000390
1	herbert	0.0000000
2	herbert	0.0000701
1	herds	0.0000335
2	herds	0.0000000
1	heres	0.0000138
2	heres	0.0000333
1	heritage	0.0000003
2	heritage	0.0000582
1	hermann	0.0000326
2	hermann	0.0000045
1	hero	0.0000000
2	hero	0.0000623
1	heroes	0.0000182
2	heroes	0.0000262
1	heroin	0.0000321
2	heroin	0.0000244
1	herons	0.0000335
2	herons	0.0000000
1	herrera	0.0000271
2	herrera	0.0000318
1	herrington	0.0000364
2	herrington	0.0000097
1	hes	0.0001299
2	hes	0.0004937
1	heseltine	0.0000000
2	heseltine	0.0000429
1	hezbollah	0.0002009
2	hezbollah	0.0000000
1	hicks	0.0000000
2	hicks	0.0000234
1	hid	0.0000002
2	hid	0.0000232
1	hidden	0.0000673
2	hidden	0.0000270
1	hide	0.0000244
2	hide	0.0000531
1	hiding	0.0000050
2	hiding	0.0000628
1	higgins	0.0000614
2	higgins	0.0000000
1	high	0.0013680
2	high	0.0004477
1	higher	0.0018043
2	higher	0.0000613
1	highest	0.0002920
2	highest	0.0000962
1	highlevel	0.0000000
2	highlevel	0.0000701
1	highlight	0.0000031
2	highlight	0.0000407
1	highlights	0.0000138
2	highlights	0.0000254
1	highly	0.0000822
2	highly	0.0001257
1	highranking	0.0000000
2	highranking	0.0000311
1	highrisk	0.0000391
2	highrisk	0.0000000
1	highs	0.0001171
2	highs	0.0000001
1	highschool	0.0000335
2	highschool	0.0000000
1	hightech	0.0000332
2	hightech	0.0000158
1	highway	0.0002339
2	highway	0.0000510
1	highways	0.0000129
2	highways	0.0000377
1	highyield	0.0000334
2	highyield	0.0000001
1	hijacked	0.0000315
2	hijacked	0.0000053
1	hijackers	0.0001819
2	hijackers	0.0000016
1	hijacking	0.0000837
2	hijacking	0.0000000
1	hike	0.0000353
2	hike	0.0000027
1	hikes	0.0000413
2	hikes	0.0000062
1	hildreth	0.0000726
2	hildreth	0.0000000
1	hildreths	0.0000335
2	hildreths	0.0000000
1	hill	0.0000804
2	hill	0.0001348
1	hills	0.0001266
2	hills	0.0000714
1	hillside	0.0000391
2	hillside	0.0000000
1	hilton	0.0000260
2	hilton	0.0000091
1	hindu	0.0001061
2	hindu	0.0000000
1	hindus	0.0000949
2	hindus	0.0000000
1	hinted	0.0000065
2	hinted	0.0000344
1	hire	0.0000151
2	hire	0.0000674
1	hired	0.0001260
2	hired	0.0000640
1	hiring	0.0000210
2	hiring	0.0000749
1	hirohito	0.0000232
2	hirohito	0.0000422
1	hispanic	0.0000000
2	hispanic	0.0001325
1	hispanics	0.0000003
2	hispanics	0.0000427
1	historians	0.0000000
2	historians	0.0000429
1	historic	0.0000367
2	historic	0.0000952
1	historical	0.0000570
2	historical	0.0000382
1	history	0.0001880
2	history	0.0004142
1	hit	0.0007274
2	hit	0.0001234
1	hitler	0.0000000
2	hitler	0.0000701
1	hits	0.0000837
2	hits	0.0000000
1	hitting	0.0000678
2	hitting	0.0000072
1	hittle	0.0000447
2	hittle	0.0000000
1	hiv	0.0000614
2	hiv	0.0000000
1	hoan	0.0000000
2	hoan	0.0000234
1	hodel	0.0000309
2	hodel	0.0000174
1	hoffa	0.0000000
2	hoffa	0.0000273
1	hoffman	0.0000606
2	hoffman	0.0000161
1	hogs	0.0000781
2	hogs	0.0000000
1	hold	0.0002138
2	hold	0.0003962
1	holderman	0.0000000
2	holderman	0.0000351
1	holders	0.0000407
2	holders	0.0000105
1	holding	0.0002092
2	holding	0.0002475
1	holdings	0.0001724
2	holdings	0.0000005
1	holds	0.0000774
2	holds	0.0001018
1	hole	0.0000644
2	hole	0.0000174
1	holes	0.0000289
2	holes	0.0000149
1	holiday	0.0001803
2	holiday	0.0000417
1	holidays	0.0000455
2	holidays	0.0000033
1	holland	0.0000501
2	holland	0.0000001
1	holloway	0.0000335
2	holloway	0.0000000
1	holly	0.0000082
2	holly	0.0000177
1	hollywood	0.0000697
2	hollywood	0.0000527
1	holmes	0.0000267
2	holmes	0.0000398
1	holocaust	0.0000000
2	holocaust	0.0000701
1	holy	0.0000014
2	holy	0.0000925
1	holyoke	0.0000335
2	holyoke	0.0000000
1	home	0.0011506
2	home	0.0013124
1	homeland	0.0000000
2	homeland	0.0000935
1	homeless	0.0004114
2	homeless	0.0000440
1	homelessness	0.0000215
2	homelessness	0.0000239
1	homemade	0.0000277
2	homemade	0.0000079
1	homes	0.0007208
2	homes	0.0000540
1	hometown	0.0000153
2	hometown	0.0000439
1	homicide	0.0000889
2	homicide	0.0000003
1	homosexual	0.0000400
2	homosexual	0.0000383
1	homosexuality	0.0000000
2	homosexuality	0.0000273
1	homosexuals	0.0000000
2	homosexuals	0.0000390
1	honduran	0.0000278
2	honduran	0.0001091
1	honduras	0.0000451
2	honduras	0.0001399
1	honest	0.0000000
2	honest	0.0000468
1	hong	0.0002864
2	hong	0.0000183
1	honor	0.0000068
2	honor	0.0001121
1	honoraria	0.0000000
2	honoraria	0.0000429
1	honorary	0.0000032
2	honorary	0.0000328
1	honored	0.0000163
2	honored	0.0000626
1	honoring	0.0000000
2	honoring	0.0000312
1	honors	0.0000000
2	honors	0.0000273
1	hood	0.0000598
2	hood	0.0000089
1	hook	0.0000201
2	hook	0.0000171
1	hooked	0.0000147
2	hooked	0.0000131
1	hooks	0.0000170
2	hooks	0.0000193
1	hoover	0.0000024
2	hoover	0.0000217
1	hope	0.0001425
2	hope	0.0005356
1	hoped	0.0000858
2	hoped	0.0001700
1	hopeful	0.0000106
2	hopeful	0.0000627
1	hopes	0.0001796
2	hopes	0.0002369
1	hoping	0.0000311
2	hoping	0.0001068
1	hopkins	0.0000607
2	hopkins	0.0000083
1	hoppe	0.0000000
2	hoppe	0.0000312
1	horizon	0.0000174
2	horizon	0.0000229
1	hormone	0.0000045
2	hormone	0.0000514
1	horn	0.0000148
2	horn	0.0000403
1	horror	0.0000447
2	horror	0.0000000
1	horse	0.0000298
2	horse	0.0000259
1	horton	0.0000000
2	horton	0.0000468
1	hose	0.0000502
2	hose	0.0000000
1	hoses	0.0000335
2	hoses	0.0000000
1	hosni	0.0000000
2	hosni	0.0000234
1	hospital	0.0010178
2	hospital	0.0003843
1	hospitalized	0.0000796
2	hospitalized	0.0000457
1	hospitals	0.0003433
2	hospitals	0.0000214
1	host	0.0000000
2	host	0.0001130
1	hostage	0.0000779
2	hostage	0.0001638
1	hostages	0.0000711
2	hostages	0.0003166
1	hosted	0.0000004
2	hosted	0.0000231
1	hostile	0.0001237
2	hostile	0.0000500
1	hostility	0.0000052
2	hostility	0.0000197
1	hot	0.0002900
2	hot	0.0000352
1	hotel	0.0001312
2	hotel	0.0003175
1	hotels	0.0000599
2	hotels	0.0000361
1	hottest	0.0000390
2	hottest	0.0000000
1	houphouetboigny	0.0000000
2	houphouetboigny	0.0000273
1	hour	0.0004834
2	hour	0.0000912
1	hourly	0.0000391
2	hourly	0.0000000
1	hours	0.0009271
2	hours	0.0004126
1	house	0.0003848
2	house	0.0025754
1	housed	0.0000106
2	housed	0.0000237
1	household	0.0000696
2	household	0.0000527
1	households	0.0000270
2	households	0.0000123
1	houses	0.0002564
2	houses	0.0001054
1	housing	0.0002833
2	housing	0.0002970
1	houston	0.0002840
2	houston	0.0000667
1	houstonbased	0.0000502
2	houstonbased	0.0000000
1	houstoun	0.0000502
2	houstoun	0.0000000
1	hovered	0.0000335
2	hovered	0.0000000
1	howard	0.0000472
2	howard	0.0001462
1	howell	0.0000000
2	howell	0.0000273
1	hoyt	0.0000000
2	hoyt	0.0000312
1	hrawi	0.0000391
2	hrawi	0.0000000
1	hrb	0.0000335
2	hrb	0.0000000
1	hsn	0.0000446
2	hsn	0.0000000
1	hu	0.0000391
2	hu	0.0000000
1	hub	0.0000335
2	hub	0.0000000
1	hubbert	0.0000000
2	hubbert	0.0000857
1	hubble	0.0001005
2	hubble	0.0000000
1	hubert	0.0000000
2	hubert	0.0000312
1	hud	0.0000249
2	hud	0.0000177
1	hudson	0.0001120
2	hudson	0.0000543
1	huge	0.0003188
2	huge	0.0000969
1	hugh	0.0000494
2	hugh	0.0000006
1	hughes	0.0000414
2	hughes	0.0000139
1	hull	0.0000338
2	hull	0.0000310
1	human	0.0001239
2	human	0.0008408
1	humanitarian	0.0000000
2	humanitarian	0.0001247
1	humanity	0.0000000
2	humanity	0.0000351
1	humans	0.0000762
2	humans	0.0000014
1	humberto	0.0000000
2	humberto	0.0000429
1	humidity	0.0000447
2	humidity	0.0000000
1	humor	0.0000000
2	humor	0.0000584
1	humphrey	0.0000000
2	humphrey	0.0002221
1	humphreys	0.0000000
2	humphreys	0.0000351
1	hundred	0.0000699
2	hundred	0.0001109
1	hundreds	0.0002429
2	hundreds	0.0003837
1	hungarian	0.0000146
2	hungarian	0.0000132
1	hungary	0.0000000
2	hungary	0.0000701
1	hunger	0.0000009
2	hunger	0.0000851
1	hunt	0.0000000
2	hunt	0.0001208
1	hunter	0.0000220
2	hunter	0.0000781
1	hunters	0.0000077
2	hunters	0.0000297
1	hunthausen	0.0000000
2	hunthausen	0.0000351
1	hunting	0.0000180
2	hunting	0.0000303
1	hurd	0.0000000
2	hurd	0.0000351
1	hurled	0.0000568
2	hurled	0.0000188
1	hurling	0.0000324
2	hurling	0.0000008
1	hurricane	0.0000893
2	hurricane	0.0000000
1	hurricanes	0.0000335
2	hurricanes	0.0000000
1	hurt	0.0001725
2	hurt	0.0001990
1	hurting	0.0000173
2	hurting	0.0000308
1	husband	0.0000759
2	husband	0.0003094
1	husbands	0.0000000
2	husbands	0.0000545
1	hussein	0.0000298
2	hussein	0.0001974
1	husseins	0.0000090
2	husseins	0.0000249
1	huts	0.0000335
2	huts	0.0000000
1	hutton	0.0000781
2	hutton	0.0000000
1	hyde	0.0000000
2	hyde	0.0000273
1	hydrogen	0.0000603
2	hydrogen	0.0000046
1	hyundai	0.0000837
2	hyundai	0.0000000
1	i	0.0014642
2	i	0.0070542
1	iacocca	0.0000447
2	iacocca	0.0000000
1	iason	0.0000000
2	iason	0.0000312
1	ibm	0.0001786
2	ibm	0.0000000
1	icahn	0.0001005
2	icahn	0.0000000
1	icc	0.0000335
2	icc	0.0000000
1	ice	0.0001990
2	ice	0.0000091
1	icing	0.0000447
2	icing	0.0000000
1	id	0.0000778
2	id	0.0001093
1	idaho	0.0001145
2	idaho	0.0000564
1	idea	0.0000762
2	idea	0.0003169
1	ideal	0.0000367
2	ideal	0.0000212
1	ideas	0.0000205
2	ideas	0.0000948
1	identical	0.0000091
2	identical	0.0000210
1	identification	0.0000283
2	identification	0.0000854
1	identified	0.0002776
2	identified	0.0003244
1	identify	0.0000841
2	identify	0.0000699
1	identities	0.0000355
2	identities	0.0000103
1	identity	0.0000415
2	identity	0.0000489
1	ideological	0.0000000
2	ideological	0.0000468
1	ideology	0.0000000
2	ideology	0.0000273
1	iditarod	0.0000391
2	iditarod	0.0000000
1	ienner	0.0000391
2	ienner	0.0000000
1	ignited	0.0000488
2	ignited	0.0000010
1	ignore	0.0000000
2	ignore	0.0000351
1	ignored	0.0000353
2	ignored	0.0000805
1	ignoring	0.0000000
2	ignoring	0.0000273
1	ii	0.0000805
2	ii	0.0005048
1	iii	0.0000023
2	iii	0.0003062
1	iliescu	0.0000000
2	iliescu	0.0000351
1	ill	0.0001958
2	ill	0.0002607
1	illegal	0.0000258
2	illegal	0.0004028
1	illegally	0.0000089
2	illegally	0.0001029
1	illinois	0.0001301
2	illinois	0.0002170
1	illness	0.0000499
2	illness	0.0001288
1	illustrated	0.0000374
2	illustrated	0.0000128
1	im	0.0001334
2	im	0.0009237
1	image	0.0000742
2	image	0.0000845
1	images	0.0000280
2	images	0.0000311
1	imagination	0.0000172
2	imagination	0.0000114
1	imagine	0.0000000
2	imagine	0.0000428
1	imbalance	0.0000780
2	imbalance	0.0000001
1	imf	0.0000502
2	imf	0.0000000
1	immediate	0.0001992
2	immediate	0.0001999
1	immediately	0.0003456
2	immediately	0.0003276
1	immigrant	0.0000341
2	immigrant	0.0000035
1	immigrants	0.0000480
2	immigrants	0.0001106
1	immigration	0.0000006
2	immigration	0.0002489
1	imminent	0.0000385
2	imminent	0.0000433
1	immune	0.0002074
2	immune	0.0000072
1	immunity	0.0000000
2	immunity	0.0000584
1	immunized	0.0000000
2	immunized	0.0000273
1	impact	0.0002648
2	impact	0.0001541
1	impassioned	0.0000000
2	impassioned	0.0000234
1	impeached	0.0000000
2	impeached	0.0000234
1	impeachment	0.0000000
2	impeachment	0.0001091
1	impediments	0.0000045
2	impediments	0.0000203
1	impending	0.0000513
2	impending	0.0000110
1	imperial	0.0000092
2	imperial	0.0000598
1	implant	0.0000366
2	implant	0.0000017
1	implanted	0.0000000
2	implanted	0.0000312
1	implement	0.0000342
2	implement	0.0000618
1	implementation	0.0000000
2	implementation	0.0000390
1	implemented	0.0000002
2	implemented	0.0000466
1	implementing	0.0000592
2	implementing	0.0000055
1	implicated	0.0000000
2	implicated	0.0000351
1	implications	0.0000349
2	implications	0.0000302
1	implied	0.0000065
2	implied	0.0000227
1	import	0.0000684
2	import	0.0000146
1	importance	0.0000246
2	importance	0.0000919
1	important	0.0003320
2	important	0.0004968
1	imported	0.0001145
2	imported	0.0000058
1	importers	0.0000335
2	importers	0.0000000
1	imports	0.0003656
2	imports	0.0000448
1	impose	0.0000372
2	impose	0.0001143
1	imposed	0.0000000
2	imposed	0.0002376
1	imposing	0.0000168
2	imposing	0.0000350
1	impossible	0.0000923
2	impossible	0.0000797
1	impoverished	0.0000146
2	impoverished	0.0000366
1	impressed	0.0000336
2	impressed	0.0000155
1	impression	0.0000246
2	impression	0.0000647
1	impressive	0.0000317
2	impressive	0.0000090
1	imprisoned	0.0000000
2	imprisoned	0.0000584
1	imprisonment	0.0000000
2	imprisonment	0.0000468
1	improper	0.0000275
2	improper	0.0000626
1	improperly	0.0000000
2	improperly	0.0000429
1	improve	0.0001302
2	improve	0.0001818
1	improved	0.0001171
2	improved	0.0001559
1	improvement	0.0001518
2	improvement	0.0000148
1	improvements	0.0000842
2	improvements	0.0000309
1	improving	0.0000627
2	improving	0.0000458
1	inability	0.0000374
2	inability	0.0000285
1	inaccurate	0.0000160
2	inaccurate	0.0000200
1	inadequate	0.0000664
2	inadequate	0.0000121
1	inappropriate	0.0000000
2	inappropriate	0.0000312
1	inaugurated	0.0000080
2	inaugurated	0.0000217
1	inauguration	0.0000004
2	inauguration	0.0000659
1	inc	0.0017890
2	inc	0.0000448
1	incentive	0.0000086
2	incentive	0.0000368
1	incentives	0.0000658
2	incentives	0.0000086
1	incest	0.0000000
2	incest	0.0000273
1	inch	0.0001284
2	inch	0.0000000
1	inched	0.0000284
2	inched	0.0000074
1	inches	0.0003663
2	inches	0.0000054
1	incident	0.0001782
2	incident	0.0002223
1	incidents	0.0000727
2	incidents	0.0000895
1	inclined	0.0000000
2	inclined	0.0000311
1	income	0.0007745
2	income	0.0000749
1	incomes	0.0000841
2	incomes	0.0000114
1	incoming	0.0000000
2	incoming	0.0000351
1	incorporated	0.0000436
2	incorporated	0.0000124
1	increase	0.0013349
2	increase	0.0001123
1	increased	0.0006371
2	increased	0.0001241
1	increases	0.0004514
2	increases	0.0001174
1	increasing	0.0002832
2	increasing	0.0000906
1	increasingly	0.0000815
2	increasingly	0.0000600
1	incredible	0.0000176
2	incredible	0.0000266
1	incumbent	0.0000000
2	incumbent	0.0000545
1	incumbents	0.0000000
2	incumbents	0.0000429
1	incurred	0.0000147
2	incurred	0.0000131
1	indefinite	0.0000502
2	indefinite	0.0000000
1	indefinitely	0.0000368
2	indefinitely	0.0000016
1	independence	0.0000000
2	independence	0.0004714
1	independent	0.0001002
2	independent	0.0004833
1	independently	0.0000091
2	independently	0.0000326
1	independents	0.0000000
2	independents	0.0000312
1	index	0.0012000
2	index	0.0000000
1	india	0.0003851
2	india	0.0000000
1	indian	0.0003003
2	indian	0.0000982
1	indiana	0.0000208
2	indiana	0.0001335
1	indianapolis	0.0000531
2	indianapolis	0.0000214
1	indians	0.0000547
2	indians	0.0000397
1	indias	0.0000686
2	indias	0.0000067
1	indicate	0.0001162
2	indicate	0.0000513
1	indicated	0.0001706
2	indicated	0.0003367
1	indicates	0.0000609
2	indicates	0.0000315
1	indicating	0.0000863
2	indicating	0.0000294
1	indication	0.0000999
2	indication	0.0000628
1	indications	0.0000715
2	indications	0.0000436
1	indicator	0.0000359
2	indicator	0.0000022
1	indicators	0.0000781
2	indicators	0.0000000
1	indicted	0.0000001
2	indicted	0.0001363
1	indictment	0.0000000
2	indictment	0.0003312
1	indictments	0.0000093
2	indictments	0.0000442
1	indirect	0.0000165
2	indirect	0.0000274
1	indirectly	0.0000009
2	indirectly	0.0000500
1	individual	0.0001561
2	individual	0.0001248
1	individuals	0.0001475
2	individuals	0.0001191
1	indonesia	0.0000383
2	indonesia	0.0000044
1	induced	0.0000000
2	induced	0.0000234
1	industrial	0.0006064
2	industrial	0.0000404
1	industrialist	0.0000052
2	industrialist	0.0000197
1	industrialized	0.0000484
2	industrialized	0.0000246
1	industrials	0.0002847
2	industrials	0.0000000
1	industries	0.0004098
2	industries	0.0000490
1	industry	0.0014034
2	industry	0.0000568
1	industrys	0.0000710
2	industrys	0.0000011
1	ineligible	0.0000000
2	ineligible	0.0000351
1	inevitable	0.0000450
2	inevitable	0.0000075
1	inevitably	0.0000336
2	inevitably	0.0000155
1	inf	0.0000000
2	inf	0.0000545
1	infant	0.0000370
2	infant	0.0000287
1	infantry	0.0000174
2	infantry	0.0000229
1	infants	0.0000702
2	infants	0.0000173
1	infected	0.0001674
2	infected	0.0000000
1	infection	0.0000735
2	infection	0.0000071
1	infections	0.0000668
2	infections	0.0000001
1	infectious	0.0000019
2	infectious	0.0000221
1	inflated	0.0000614
2	inflated	0.0000000
1	inflation	0.0007106
2	inflation	0.0000182
1	inflationary	0.0000558
2	inflationary	0.0000000
1	influence	0.0000963
2	influence	0.0002211
1	influenced	0.0000476
2	influenced	0.0000174
1	influential	0.0000000
2	influential	0.0000506
1	influx	0.0000003
2	influx	0.0000348
1	inform	0.0000260
2	inform	0.0000247
1	informal	0.0000000
2	informal	0.0000506
1	informant	0.0000000
2	informant	0.0000390
1	information	0.0006014
2	information	0.0006049
1	informed	0.0000649
2	informed	0.0000794
1	inhabitants	0.0000072
2	inhabitants	0.0000184
1	inheritance	0.0001239
2	inheritance	0.0000187
1	inherited	0.0000125
2	inherited	0.0000186
1	initial	0.0001879
2	initial	0.0000636
1	initially	0.0000483
2	initially	0.0001221
1	initiated	0.0000000
2	initiated	0.0000234
1	initiative	0.0000172
2	initiative	0.0001399
1	initiatives	0.0000085
2	initiatives	0.0000525
1	injected	0.0000303
2	injected	0.0000139
1	injection	0.0000025
2	injection	0.0000216
1	injunction	0.0000493
2	injunction	0.0000280
1	injured	0.0006517
2	injured	0.0001295
1	injuries	0.0004744
2	injuries	0.0000000
1	injuring	0.0001081
2	injuring	0.0000142
1	injury	0.0001375
2	injury	0.0000092
1	injustice	0.0000000
2	injustice	0.0000506
1	ink	0.0000204
2	ink	0.0000130
1	inkatha	0.0000000
2	inkatha	0.0000584
1	inland	0.0000558
2	inland	0.0000000
1	inlet	0.0000447
2	inlet	0.0000000
1	inmate	0.0000001
2	inmate	0.0000740
1	inmates	0.0000000
2	inmates	0.0001597
1	inn	0.0000276
2	inn	0.0000041
1	innocence	0.0000000
2	innocence	0.0000312
1	innocent	0.0000000
2	innocent	0.0002143
1	innovative	0.0000423
2	innovative	0.0000017
1	inquiries	0.0000273
2	inquiries	0.0000160
1	inquiry	0.0000255
2	inquiry	0.0000874
1	ins	0.0000000
2	ins	0.0000818
1	insane	0.0000000
2	insane	0.0000234
1	inside	0.0001855
2	inside	0.0002173
1	insider	0.0000149
2	insider	0.0000364
1	insignificant	0.0000000
2	insignificant	0.0000312
1	insist	0.0000000
2	insist	0.0000312
1	insisted	0.0000025
2	insisted	0.0001463
1	insistence	0.0000125
2	insistence	0.0000224
1	insisting	0.0000001
2	insisting	0.0000428
1	insists	0.0000030
2	insists	0.0000407
1	insolvent	0.0000710
2	insolvent	0.0000011
1	inspect	0.0000100
2	inspect	0.0000203
1	inspection	0.0001842
2	inspection	0.0000000
1	inspections	0.0000557
2	inspections	0.0000001
1	inspector	0.0000644
2	inspector	0.0000174
1	inspectors	0.0001226
2	inspectors	0.0000002
1	inspiration	0.0000000
2	inspiration	0.0000273
1	inspired	0.0000327
2	inspired	0.0000356
1	instability	0.0000000
2	instability	0.0000234
1	install	0.0000398
2	install	0.0000267
1	installation	0.0000173
2	installation	0.0000191
1	installations	0.0000250
2	installations	0.0000371
1	installed	0.0000642
2	installed	0.0000292
1	installing	0.0000356
2	installing	0.0000024
1	instance	0.0000434
2	instance	0.0000320
1	instances	0.0000331
2	instances	0.0000509
1	instant	0.0000014
2	instant	0.0000263
1	institute	0.0004217
2	institute	0.0000563
1	instituted	0.0000124
2	instituted	0.0000186
1	institutes	0.0000354
2	institutes	0.0000064
1	institution	0.0000493
2	institution	0.0000825
1	institutional	0.0000729
2	institutional	0.0000309
1	institutions	0.0002390
2	institutions	0.0001253
1	instructed	0.0000000
2	instructed	0.0000468
1	instruction	0.0000108
2	instruction	0.0000275
1	instructions	0.0000403
2	instructions	0.0000342
1	instructor	0.0000558
2	instructor	0.0000000
1	instructors	0.0000000
2	instructors	0.0000234
1	instrument	0.0000502
2	instrument	0.0000000
1	instruments	0.0000614
2	instruments	0.0000000
1	insufficient	0.0000316
2	insufficient	0.0000325
1	insulation	0.0000391
2	insulation	0.0000000
1	insult	0.0000000
2	insult	0.0000312
1	insults	0.0000000
2	insults	0.0000273
1	insurance	0.0005922
2	insurance	0.0000658
1	insured	0.0000471
2	insured	0.0000100
1	insurer	0.0000011
2	insurer	0.0000265
1	insurgency	0.0000000
2	insurgency	0.0000429
1	insurgents	0.0000000
2	insurgents	0.0000312
1	integrated	0.0000193
2	integrated	0.0000371
1	integrity	0.0000000
2	integrity	0.0000701
1	intellectual	0.0000000
2	intellectual	0.0000506
1	intelligence	0.0000209
2	intelligence	0.0001958
1	intend	0.0000344
2	intend	0.0000539
1	intended	0.0000655
2	intended	0.0001725
1	intends	0.0000308
2	intends	0.0000486
1	intense	0.0000409
2	intense	0.0000767
1	intensified	0.0000407
2	intensified	0.0000222
1	intensify	0.0000000
2	intensify	0.0000234
1	intensive	0.0000661
2	intensive	0.0000201
1	intent	0.0000194
2	intent	0.0000839
1	intention	0.0000504
2	intention	0.0001090
1	intentionally	0.0000000
2	intentionally	0.0000234
1	intentions	0.0000222
2	intentions	0.0000157
1	inter	0.0000140
2	inter	0.0000136
1	interagency	0.0000157
2	interagency	0.0000163
1	intercontinental	0.0000214
2	intercontinental	0.0000162
1	interest	0.0014497
2	interest	0.0002465
1	interested	0.0001673
2	interested	0.0000936
1	interesting	0.0000007
2	interesting	0.0000346
1	interests	0.0000643
2	interests	0.0002395
1	interfere	0.0000000
2	interfere	0.0000390
1	interference	0.0000125
2	interference	0.0000146
1	interim	0.0000130
2	interim	0.0000767
1	interior	0.0000653
2	interior	0.0002037
1	intermediate	0.0000719
2	intermediate	0.0000043
1	internal	0.0001053
2	internal	0.0001564
1	international	0.0009661
2	international	0.0008334
1	interpretation	0.0000000
2	interpretation	0.0000545
1	interpreted	0.0000396
2	interpreted	0.0000113
1	interrupted	0.0000210
2	interrupted	0.0000438
1	intersection	0.0000391
2	intersection	0.0000000
1	interstate	0.0001494
2	interstate	0.0000165
1	intervene	0.0000447
2	intervene	0.0000000
1	intervened	0.0000000
2	intervened	0.0000390
1	intervention	0.0000940
2	intervention	0.0000552
1	interview	0.0002177
2	interview	0.0006467
1	interviewed	0.0000492
2	interviewed	0.0001098
1	interviews	0.0000280
2	interviews	0.0001090
1	intimidate	0.0000000
2	intimidate	0.0000273
1	intimidation	0.0000000
2	intimidation	0.0000312
1	intravenous	0.0000695
2	intravenous	0.0000022
1	introduce	0.0000040
2	introduce	0.0000401
1	introduced	0.0001134
2	introduced	0.0002170
1	introducing	0.0000000
2	introducing	0.0000234
1	introduction	0.0000507
2	introduction	0.0000269
1	invade	0.0000000
2	invade	0.0000234
1	invaded	0.0000469
2	invaded	0.0000997
1	invasion	0.0001198
2	invasion	0.0002982
1	invented	0.0000093
2	invented	0.0000169
1	inventories	0.0001507
2	inventories	0.0000000
1	inventory	0.0001395
2	inventory	0.0000000
1	invest	0.0000362
2	invest	0.0000059
1	invested	0.0000643
2	invested	0.0000097
1	investigate	0.0000291
2	investigate	0.0001433
1	investigated	0.0000454
2	investigated	0.0000696
1	investigating	0.0001055
2	investigating	0.0001095
1	investigation	0.0002512
2	investigation	0.0006778
1	investigations	0.0000516
2	investigations	0.0001003
1	investigative	0.0000076
2	investigative	0.0000804
1	investigator	0.0000254
2	investigator	0.0000446
1	investigators	0.0003392
2	investigators	0.0001295
1	investing	0.0000296
2	investing	0.0000105
1	investment	0.0006008
2	investment	0.0001300
1	investments	0.0001120
2	investments	0.0000816
1	investor	0.0001618
2	investor	0.0000000
1	investors	0.0007839
2	investors	0.0000022
1	invitation	0.0000000
2	invitation	0.0000662
1	invited	0.0000282
2	invited	0.0001128
1	inviting	0.0000126
2	inviting	0.0000185
1	involuntary	0.0000001
2	involuntary	0.0000272
1	involve	0.0000577
2	involve	0.0000455
1	involved	0.0002958
2	involved	0.0004792
1	involvement	0.0000361
2	involvement	0.0001618
1	involves	0.0000537
2	involves	0.0000443
1	involving	0.0001843
2	involving	0.0001636
1	iowa	0.0001165
2	iowa	0.0000784
1	ira	0.0001972
2	ira	0.0000337
1	iran	0.0000338
2	iran	0.0005881
1	irancontra	0.0000000
2	irancontra	0.0001325
1	iranian	0.0000932
2	iranian	0.0002232
1	iranians	0.0000026
2	iranians	0.0000449
1	iraniraq	0.0000059
2	iraniraq	0.0000193
1	irans	0.0000000
2	irans	0.0000740
1	iraq	0.0000907
2	iraq	0.0008951
1	iraqi	0.0001115
2	iraqi	0.0006312
1	iraqis	0.0000146
2	iraqis	0.0000638
1	iraqs	0.0000445
2	iraqs	0.0001910
1	ireland	0.0002495
2	ireland	0.0000012
1	irish	0.0001563
2	irish	0.0000701
1	irishman	0.0000391
2	irishman	0.0000000
1	iron	0.0000327
2	iron	0.0000551
1	irresponsible	0.0000000
2	irresponsible	0.0000234
1	irreversible	0.0000000
2	irreversible	0.0000351
1	irrigation	0.0000335
2	irrigation	0.0000000
1	irs	0.0002854
2	irs	0.0000111
1	irvine	0.0000000
2	irvine	0.0000234
1	irving	0.0001451
2	irving	0.0000000
1	irvings	0.0000335
2	irvings	0.0000000
1	isaac	0.0000095
2	isaac	0.0000245
1	islam	0.0000008
2	islam	0.0000501
1	islamic	0.0000051
2	islamic	0.0001328
1	island	0.0003710
2	island	0.0001813
1	islands	0.0001079
2	islands	0.0001390
1	isnt	0.0001507
2	isnt	0.0001480
1	isolated	0.0000803
2	isolated	0.0000413
1	isolation	0.0000075
2	isolation	0.0000338
1	israel	0.0000000
2	israel	0.0008298
1	israeli	0.0000016
2	israeli	0.0005677
1	israelis	0.0000001
2	israelis	0.0001129
1	israels	0.0000000
2	israels	0.0001325
1	issue	0.0002821
2	issue	0.0009563
1	issued	0.0003265
2	issued	0.0003682
1	issues	0.0007674
2	issues	0.0004890
1	issuing	0.0000161
2	issuing	0.0000238
1	italian	0.0003885
2	italian	0.0000405
1	italy	0.0001119
2	italy	0.0000972
1	italys	0.0000446
2	italys	0.0000001
1	item	0.0000279
2	item	0.0000273
1	items	0.0001977
2	items	0.0000646
1	itll	0.0000009
2	itll	0.0000539
1	ivan	0.0000000
2	ivan	0.0000584
1	ive	0.0000900
2	ive	0.0002917
1	ives	0.0000000
2	ives	0.0000312
1	ivory	0.0000122
2	ivory	0.0000265
1	izvestia	0.0000000
2	izvestia	0.0000273
1	j	0.0001995
2	j	0.0002270
1	jack	0.0001123
2	jack	0.0001632
1	jacket	0.0000002
2	jacket	0.0000272
1	jackets	0.0000086
2	jackets	0.0000252
1	jackpot	0.0001284
2	jackpot	0.0000000
1	jackson	0.0000000
2	jackson	0.0008727
1	jacksons	0.0000000
2	jacksons	0.0001208
1	jacksonville	0.0000443
2	jacksonville	0.0000080
1	jacobs	0.0000446
2	jacobs	0.0000000
1	jacques	0.0000043
2	jacques	0.0000321
1	jail	0.0000000
2	jail	0.0003818
1	jailed	0.0000000
2	jailed	0.0001558
1	jails	0.0000000
2	jails	0.0000468
1	jal	0.0000558
2	jal	0.0000000
1	james	0.0003575
2	james	0.0006348
1	jammed	0.0000586
2	jammed	0.0000370
1	jamming	0.0000000
2	jamming	0.0000506
1	jammukashmir	0.0000670
2	jammukashmir	0.0000000
1	jan	0.0002260
2	jan	0.0003565
1	jane	0.0000156
2	jane	0.0000515
1	janet	0.0000235
2	janet	0.0000265
1	january	0.0005316
2	january	0.0002328
1	japan	0.0008329
2	japan	0.0002758
1	japanese	0.0008728
2	japanese	0.0001544
1	japans	0.0001926
2	japans	0.0000603
1	jaruzelski	0.0000000
2	jaruzelski	0.0000623
1	jarvik	0.0000000
2	jarvik	0.0000584
1	jason	0.0000183
2	jason	0.0000184
1	javier	0.0000000
2	javier	0.0000429
1	jay	0.0000258
2	jay	0.0000287
1	jazz	0.0000000
2	jazz	0.0000311
1	jean	0.0000203
2	jean	0.0000365
1	jeanclaude	0.0000000
2	jeanclaude	0.0000351
1	jeans	0.0000009
2	jeans	0.0000422
1	jeep	0.0000445
2	jeep	0.0000001
1	jeff	0.0000356
2	jeff	0.0000414
1	jeffer	0.0000000
2	jeffer	0.0000273
1	jefferson	0.0000531
2	jefferson	0.0000136
1	jeffrey	0.0000357
2	jeffrey	0.0000101
1	jenkins	0.0000689
2	jenkins	0.0000065
1	jeopardize	0.0000203
2	jeopardize	0.0000209
1	jeopardy	0.0000075
2	jeopardy	0.0000182
1	jeremy	0.0000455
2	jeremy	0.0000072
1	jerry	0.0000325
2	jerry	0.0000747
1	jersey	0.0001408
2	jersey	0.0001510
1	jerusalem	0.0000008
2	jerusalem	0.0001397
1	jesse	0.0000000
2	jesse	0.0001987
1	jessica	0.0000000
2	jessica	0.0000234
1	jesus	0.0000000
2	jesus	0.0001169
1	jet	0.0002557
2	jet	0.0000358
1	jetliner	0.0001172
2	jetliner	0.0000000
1	jets	0.0001727
2	jets	0.0000003
1	jew	0.0000000
2	jew	0.0000312
1	jewelry	0.0000987
2	jewelry	0.0000363
1	jewish	0.0000121
2	jewish	0.0004201
1	jews	0.0000000
2	jews	0.0002299
1	jiang	0.0000000
2	jiang	0.0000273
1	jim	0.0001909
2	jim	0.0001512
1	jimmy	0.0000000
2	jimmy	0.0001325
1	joan	0.0000962
2	joan	0.0000185
1	joaquin	0.0000000
2	joaquin	0.0000234
1	job	0.0003971
2	job	0.0006111
1	jobless	0.0000458
2	jobless	0.0000109
1	jobs	0.0005186
2	jobs	0.0003081
1	joe	0.0000912
2	joe	0.0000961
1	joel	0.0000399
2	joel	0.0000228
1	joey	0.0000000
2	joey	0.0000273
1	johannesburg	0.0000000
2	johannesburg	0.0000662
1	john	0.0006097
2	john	0.0011172
1	johns	0.0000488
2	johns	0.0000166
1	johnson	0.0002624
2	johnson	0.0001013
1	johnston	0.0000344
2	johnston	0.0000188
1	join	0.0000078
2	join	0.0002166
1	joined	0.0001492
2	joined	0.0002893
1	joining	0.0000305
2	joining	0.0000878
1	joint	0.0003180
2	joint	0.0002105
1	jointly	0.0000191
2	jointly	0.0000451
1	joints	0.0000447
2	joints	0.0000000
1	joked	0.0000089
2	joked	0.0000211
1	jolla	0.0000335
2	jolla	0.0000000
1	jon	0.0000208
2	jon	0.0000283
1	jonathan	0.0000114
2	jonathan	0.0000271
1	jones	0.0006463
2	jones	0.0000748
1	jordan	0.0000140
2	jordan	0.0001344
1	jorge	0.0000000
2	jorge	0.0000234
1	jose	0.0000428
2	jose	0.0001259
1	josef	0.0000000
2	josef	0.0000351
1	joseph	0.0000692
2	joseph	0.0002828
1	josephs	0.0000372
2	josephs	0.0000091
1	journal	0.0001213
2	journal	0.0000906
1	journalism	0.0000000
2	journalism	0.0000312
1	journalist	0.0000000
2	journalist	0.0000779
1	journalists	0.0000117
2	journalists	0.0001788
1	journey	0.0000871
2	journey	0.0000094
1	joy	0.0000000
2	joy	0.0000506
1	joyes	0.0000335
2	joyes	0.0000000
1	jr	0.0001566
2	jr	0.0004595
1	juan	0.0000691
2	juan	0.0000608
1	judge	0.0000369
2	judge	0.0012833
1	judged	0.0000000
2	judged	0.0000234
1	judges	0.0000006
2	judges	0.0002372
1	judgment	0.0000173
2	judgment	0.0001594
1	judicial	0.0000001
2	judicial	0.0001129
1	judiciary	0.0000000
2	judiciary	0.0000857
1	judith	0.0000000
2	judith	0.0000234
1	judy	0.0000636
2	judy	0.0000063
1	juice	0.0000447
2	juice	0.0000000
1	julie	0.0000365
2	julie	0.0000018
1	julio	0.0000245
2	julio	0.0000063
1	july	0.0009052
2	july	0.0003344
1	jumbo	0.0000558
2	jumbo	0.0000000
1	jump	0.0001404
2	jump	0.0000111
1	jumped	0.0003349
2	jumped	0.0000000
1	jumping	0.0000335
2	jumping	0.0000000
1	june	0.0007651
2	june	0.0005413
1	jungle	0.0000403
2	jungle	0.0000109
1	junior	0.0000000
2	junior	0.0000273
1	junk	0.0001429
2	junk	0.0000210
1	jupiter	0.0000614
2	jupiter	0.0000000
1	jurisdiction	0.0000207
2	jurisdiction	0.0000596
1	jurors	0.0000000
2	jurors	0.0001130
1	jury	0.0000000
2	jury	0.0005727
1	just	0.0017286
2	just	0.0011933
1	justice	0.0000003
2	justice	0.0007751
1	justices	0.0000000
2	justices	0.0001208
1	justification	0.0000142
2	justification	0.0000174
1	justified	0.0000054
2	justified	0.0000352
1	justify	0.0000000
2	justify	0.0000468
1	juvenile	0.0000391
2	juvenile	0.0000000
1	k	0.0000728
2	k	0.0000544
1	kabul	0.0000000
2	kabul	0.0000429
1	kahane	0.0000446
2	kahane	0.0000000
1	kahn	0.0000335
2	kahn	0.0000000
1	kaifu	0.0000011
2	kaifu	0.0000499
1	kalikow	0.0000000
2	kalikow	0.0000234
1	kalugin	0.0000000
2	kalugin	0.0000312
1	kansas	0.0001681
2	kansas	0.0000930
1	karen	0.0000556
2	karen	0.0000235
1	karl	0.0000227
2	karl	0.0000153
1	karpov	0.0000000
2	karpov	0.0000857
1	kashmir	0.0001340
2	kashmir	0.0000000
1	kasparov	0.0000000
2	kasparov	0.0000818
1	kassebaum	0.0000000
2	kassebaum	0.0000234
1	kathleen	0.0000273
2	kathleen	0.0000199
1	katyn	0.0000000
2	katyn	0.0000234
1	kaunda	0.0000000
2	kaunda	0.0000273
1	keating	0.0000000
2	keating	0.0001675
1	keatings	0.0000000
2	keatings	0.0000390
1	keefe	0.0000062
2	keefe	0.0000229
1	keep	0.0004224
2	keep	0.0004999
1	keeping	0.0001573
2	keeping	0.0001473
1	keeps	0.0000613
2	keeps	0.0000390
1	keidanren	0.0000000
2	keidanren	0.0000390
1	keith	0.0000501
2	keith	0.0000195
1	keller	0.0000614
2	keller	0.0000000
1	kelly	0.0000248
2	kelly	0.0000412
1	kemp	0.0000000
2	kemp	0.0000623
1	ken	0.0000300
2	ken	0.0000297
1	kennebunkport	0.0000000
2	kennebunkport	0.0000273
1	kennedy	0.0000733
2	kennedy	0.0003384
1	kennedys	0.0000000
2	kennedys	0.0000545
1	kenner	0.0000447
2	kenner	0.0000000
1	kenneth	0.0000342
2	kenneth	0.0000657
1	kent	0.0000075
2	kent	0.0000259
1	kentucky	0.0001343
2	kentucky	0.0000193
1	kephart	0.0000949
2	kephart	0.0000000
1	kept	0.0002443
2	kept	0.0001879
1	kevin	0.0000485
2	kevin	0.0000129
1	kevorkian	0.0000000
2	kevorkian	0.0000234
1	key	0.0003118
2	key	0.0002616
1	keys	0.0000755
2	keys	0.0000174
1	kgb	0.0000000
2	kgb	0.0001169
1	khamenei	0.0000000
2	khamenei	0.0000506
1	khan	0.0000118
2	khan	0.0000229
1	khashoggi	0.0000101
2	khashoggi	0.0000397
1	khmer	0.0000000
2	khmer	0.0000545
1	khomeini	0.0000000
2	khomeini	0.0000623
1	khrushchev	0.0000000
2	khrushchev	0.0000545
1	kick	0.0000000
2	kick	0.0000312
1	kicked	0.0000275
2	kicked	0.0000431
1	kid	0.0000004
2	kid	0.0000816
1	kidder	0.0000391
2	kidder	0.0000000
1	kidnapped	0.0000075
2	kidnapped	0.0001039
1	kidnappers	0.0000632
2	kidnappers	0.0000065
1	kidnapping	0.0000038
2	kidnapping	0.0001025
1	kidney	0.0000219
2	kidney	0.0000315
1	kids	0.0001381
2	kids	0.0000867
1	kiesner	0.0000000
2	kiesner	0.0000273
1	kill	0.0001115
2	kill	0.0002144
1	killed	0.0009218
2	killed	0.0009227
1	killer	0.0000610
2	killer	0.0000042
1	killers	0.0000001
2	killers	0.0000311
1	killing	0.0003146
2	killing	0.0003259
1	killings	0.0000016
2	killings	0.0001119
1	kills	0.0000104
2	kills	0.0000278
1	kim	0.0000001
2	kim	0.0001090
1	kimberly	0.0000297
2	kimberly	0.0000105
1	kin	0.0000257
2	kin	0.0000094
1	kind	0.0001284
2	kind	0.0003857
1	kindergarten	0.0000000
2	kindergarten	0.0000506
1	kinds	0.0000614
2	kinds	0.0000312
1	king	0.0000950
2	king	0.0004284
1	kingdom	0.0000353
2	kingdom	0.0000299
1	kings	0.0000085
2	kings	0.0000525
1	kinnock	0.0000000
2	kinnock	0.0000273
1	kirk	0.0000076
2	kirk	0.0000181
1	kiss	0.0000070
2	kiss	0.0000380
1	kitchen	0.0000405
2	kitchen	0.0000302
1	kitty	0.0000282
2	kitty	0.0000193
1	klan	0.0000000
2	klan	0.0000584
1	klaus	0.0000319
2	klaus	0.0000128
1	klein	0.0000001
2	klein	0.0000350
1	klerk	0.0000000
2	klerk	0.0001441
1	klerks	0.0000000
2	klerks	0.0000234
1	klux	0.0000000
2	klux	0.0000273
1	knew	0.0001116
2	knew	0.0002650
1	knife	0.0000381
2	knife	0.0000513
1	knight	0.0000246
2	knight	0.0000218
1	knives	0.0000132
2	knives	0.0000298
1	knock	0.0000007
2	knock	0.0000229
1	knocked	0.0000803
2	knocked	0.0000180
1	know	0.0004933
2	know	0.0009453
1	knowing	0.0000474
2	knowing	0.0000565
1	knowledge	0.0000268
2	knowledge	0.0001371
1	known	0.0004974
2	known	0.0005450
1	knows	0.0000479
2	knows	0.0001341
1	knudsen	0.0000335
2	knudsen	0.0000000
1	koch	0.0000000
2	koch	0.0000896
1	kochs	0.0000000
2	kochs	0.0000312
1	kodak	0.0000335
2	kodak	0.0000000
1	kohl	0.0000000
2	kohl	0.0001441
1	kohlberg	0.0001005
2	kohlberg	0.0000000
1	kolb	0.0000013
2	kolb	0.0000263
1	kolberg	0.0000502
2	kolberg	0.0000000
1	kong	0.0002686
2	kong	0.0000190
1	koppers	0.0000000
2	koppers	0.0000273
1	korea	0.0000634
2	korea	0.0003142
1	korean	0.0000294
2	korean	0.0003224
1	koreans	0.0000000
2	koreans	0.0000390
1	koreas	0.0000000
2	koreas	0.0001052
1	kraft	0.0000837
2	kraft	0.0000000
1	kravis	0.0001061
2	kravis	0.0000000
1	kremlin	0.0000000
2	kremlin	0.0001519
1	kremlins	0.0000000
2	kremlins	0.0000234
1	kristallnacht	0.0000000
2	kristallnacht	0.0000234
1	ku	0.0000000
2	ku	0.0000273
1	kurdish	0.0000001
2	kurdish	0.0000701
1	kurt	0.0000000
2	kurt	0.0000351
1	kuryla	0.0000335
2	kuryla	0.0000000
1	kuwait	0.0001908
2	kuwait	0.0005798
1	kuwaiti	0.0000123
2	kuwaiti	0.0000615
1	kuwaits	0.0000001
2	kuwaits	0.0000389
1	ky	0.0001036
2	ky	0.0000134
1	kyodo	0.0000401
2	kyodo	0.0000188
1	l	0.0001480
2	l	0.0001967
1	la	0.0002125
2	la	0.0000776
1	lab	0.0000781
2	lab	0.0000000
1	label	0.0000463
2	label	0.0000144
1	labeled	0.0000130
2	labeled	0.0000260
1	labels	0.0000341
2	labels	0.0000112
1	labor	0.0006193
2	labor	0.0005573
1	laboratories	0.0000949
2	laboratories	0.0000000
1	laboratory	0.0001876
2	laboratory	0.0000132
1	lack	0.0002043
2	lack	0.0001730
1	lacked	0.0000144
2	lacked	0.0000445
1	lackluster	0.0000391
2	lackluster	0.0000000
1	lacks	0.0000004
2	lacks	0.0000309
1	lady	0.0000181
2	lady	0.0000770
1	lafayette	0.0000093
2	lafayette	0.0000403
1	lafontant	0.0000000
2	lafontant	0.0000234
1	laid	0.0000840
2	laid	0.0000700
1	lake	0.0003291
2	lake	0.0000002
1	lakes	0.0001116
2	lakes	0.0000000
1	lama	0.0000000
2	lama	0.0000234
1	lambert	0.0000781
2	lambert	0.0000000
1	lamp	0.0000335
2	lamp	0.0000000
1	land	0.0004816
2	land	0.0002015
1	landed	0.0001076
2	landed	0.0000496
1	landfill	0.0000299
2	landfill	0.0000103
1	landfills	0.0000000
2	landfills	0.0000234
1	landing	0.0001898
2	landing	0.0000000
1	landings	0.0000362
2	landings	0.0000059
1	landmark	0.0000379
2	landmark	0.0000398
1	lands	0.0000435
2	lands	0.0000358
1	landscape	0.0000502
2	landscape	0.0000000
1	landslide	0.0000070
2	landslide	0.0000263
1	landslides	0.0000391
2	landslides	0.0000000
1	lane	0.0000454
2	lane	0.0000150
1	lang	0.0000558
2	lang	0.0000000
1	langley	0.0000384
2	langley	0.0000044
1	language	0.0000000
2	language	0.0001792
1	languages	0.0000000
2	languages	0.0000312
1	lanka	0.0000391
2	lanka	0.0000039
1	large	0.0006705
2	large	0.0001982
1	largely	0.0001470
2	largely	0.0001623
1	larger	0.0002416
2	larger	0.0000729
1	largescale	0.0000229
2	largescale	0.0000230
1	largest	0.0008847
2	largest	0.0002785
1	larry	0.0000993
2	larry	0.0000592
1	las	0.0000879
2	las	0.0000477
1	lashed	0.0000227
2	lashed	0.0000114
1	lasko	0.0000391
2	lasko	0.0000000
1	last	0.0036797
2	last	0.0029989
1	lasted	0.0000492
2	lasted	0.0000630
1	lasting	0.0000200
2	lasting	0.0000172
1	lastminute	0.0000284
2	lastminute	0.0000153
1	late	0.0019033
2	late	0.0003467
1	lately	0.0000417
2	lately	0.0000098
1	latest	0.0003743
2	latest	0.0001945
1	latin	0.0000403
2	latin	0.0000654
1	latter	0.0000287
2	latter	0.0000111
1	latvia	0.0000000
2	latvia	0.0000623
1	lauderdale	0.0000501
2	lauderdale	0.0000001
1	lauer	0.0000502
2	lauer	0.0000000
1	laugh	0.0000130
2	laugh	0.0000182
1	laughed	0.0000002
2	laughed	0.0000310
1	laughter	0.0000064
2	laughter	0.0000228
1	launch	0.0003943
2	launch	0.0000520
1	launched	0.0002066
2	launched	0.0001051
1	launchers	0.0000052
2	launchers	0.0000197
1	launching	0.0000665
2	launching	0.0000159
1	laundering	0.0000000
2	laundering	0.0000351
1	laura	0.0000563
2	laura	0.0000152
1	laurel	0.0000000
2	laurel	0.0000234
1	laurentiis	0.0000000
2	laurentiis	0.0000312
1	lautenberg	0.0000017
2	lautenberg	0.0000456
1	lavish	0.0000240
2	lavish	0.0000261
1	law	0.0001631
2	law	0.0015965
1	lawmaker	0.0000000
2	lawmaker	0.0000701
1	lawmakers	0.0000000
2	lawmakers	0.0004169
1	lawn	0.0000000
2	lawn	0.0000584
1	lawrence	0.0001090
2	lawrence	0.0000758
1	laws	0.0000564
2	laws	0.0003269
1	lawson	0.0000369
2	lawson	0.0000054
1	lawsuit	0.0001098
2	lawsuit	0.0002662
1	lawsuits	0.0000067
2	lawsuits	0.0001434
1	lawyer	0.0000000
2	lawyer	0.0003740
1	lawyers	0.0000000
2	lawyers	0.0004675
1	lax	0.0000390
2	lax	0.0000000
1	lay	0.0001381
2	lay	0.0000711
1	layer	0.0000391
2	layer	0.0000000
1	layers	0.0000236
2	layers	0.0000069
1	layoff	0.0000240
2	layoff	0.0000066
1	layoffs	0.0001563
2	layoffs	0.0000234
1	le	0.0000000
2	le	0.0000545
1	lead	0.0002351
2	lead	0.0004164
1	leader	0.0000276
2	leader	0.0014261
1	leaders	0.0000334
2	leaders	0.0012701
1	leadership	0.0000172
2	leadership	0.0003932
1	leading	0.0002730
2	leading	0.0003393
1	leads	0.0000432
2	leads	0.0000594
1	leaf	0.0000006
2	leaf	0.0000229
1	league	0.0000835
2	league	0.0001287
1	leahy	0.0000051
2	leahy	0.0000666
1	leak	0.0000893
2	leak	0.0000000
1	leaked	0.0000378
2	leaked	0.0000048
1	leaking	0.0000344
2	leaking	0.0000150
1	leaks	0.0000656
2	leaks	0.0000049
1	learn	0.0000476
2	learn	0.0001304
1	learned	0.0000312
2	learned	0.0001886
1	learning	0.0000315
2	learning	0.0000598
1	lease	0.0000447
2	lease	0.0000000
1	leather	0.0000044
2	leather	0.0000281
1	leave	0.0001318
2	leave	0.0006365
1	leaves	0.0000416
2	leaves	0.0000878
1	leaving	0.0002180
2	leaving	0.0002686
1	lebanese	0.0001228
2	lebanese	0.0000000
1	lebanon	0.0000953
2	lebanon	0.0001828
1	lebanons	0.0000391
2	lebanons	0.0000000
1	lech	0.0000000
2	lech	0.0000312
1	lecturer	0.0000056
2	lecturer	0.0000273
1	led	0.0003298
2	led	0.0005412
1	lee	0.0001178
2	lee	0.0001554
1	leek	0.0000558
2	leek	0.0000000
1	left	0.0005798
2	left	0.0009355
1	leftist	0.0000000
2	leftist	0.0002844
1	leftists	0.0000000
2	leftists	0.0000468
1	leftwing	0.0000000
2	leftwing	0.0000351
1	leg	0.0000991
2	leg	0.0000165
1	legacy	0.0000001
2	legacy	0.0000740
1	legal	0.0000876
2	legal	0.0005271
1	legalization	0.0000000
2	legalization	0.0000468
1	legalized	0.0000000
2	legalized	0.0000351
1	legalizing	0.0000000
2	legalizing	0.0000273
1	legally	0.0000106
2	legally	0.0000354
1	legend	0.0000122
2	legend	0.0000227
1	legislation	0.0000007
2	legislation	0.0006852
1	legislative	0.0000001
2	legislative	0.0001947
1	legislator	0.0000000
2	legislator	0.0000351
1	legislators	0.0000000
2	legislators	0.0001519
1	legislature	0.0000000
2	legislature	0.0002182
1	legislatures	0.0000000
2	legislatures	0.0000351
1	legitimacy	0.0000000
2	legitimacy	0.0000312
1	legitimate	0.0000000
2	legitimate	0.0000623
1	legs	0.0000387
2	legs	0.0000314
1	lehman	0.0001063
2	lehman	0.0000310
1	leipzig	0.0000067
2	leipzig	0.0000304
1	lend	0.0000182
2	lend	0.0000224
1	lenders	0.0000646
2	lenders	0.0000055
1	lending	0.0001434
2	lending	0.0000129
1	length	0.0000657
2	length	0.0000399
1	lengthy	0.0000287
2	lengthy	0.0000190
1	lenient	0.0000051
2	lenient	0.0000276
1	lenin	0.0000000
2	lenin	0.0000584
1	leningrad	0.0000000
2	leningrad	0.0000429
1	leo	0.0000170
2	leo	0.0000349
1	leon	0.0000158
2	leon	0.0000591
1	leonard	0.0000008
2	leonard	0.0000968
1	les	0.0000115
2	les	0.0000426
1	leslie	0.0000000
2	leslie	0.0000312
1	lesotho	0.0000004
2	lesotho	0.0000426
1	lespinasse	0.0000335
2	lespinasse	0.0000000
1	lessen	0.0000196
2	lessen	0.0000214
1	lesser	0.0000345
2	lesser	0.0000305
1	lesson	0.0000141
2	lesson	0.0000291
1	lessons	0.0000235
2	lessons	0.0000304
1	let	0.0001233
2	let	0.0004010
1	lethal	0.0000000
2	lethal	0.0000429
1	lets	0.0000303
2	lets	0.0000762
1	letter	0.0001065
2	letter	0.0004087
1	letterman	0.0000391
2	letterman	0.0000000
1	letters	0.0000302
2	letters	0.0002010
1	letting	0.0000215
2	letting	0.0000473
1	leukemia	0.0000442
2	leukemia	0.0000276
1	level	0.0007189
2	level	0.0001294
1	levels	0.0002627
2	levels	0.0000972
1	leverage	0.0000188
2	leverage	0.0000142
1	leveraged	0.0001228
2	leveraged	0.0000000
1	levin	0.0000000
2	levin	0.0000429
1	levy	0.0000008
2	levy	0.0000423
1	lewis	0.0000984
2	lewis	0.0000910
1	lexington	0.0000391
2	lexington	0.0000000
1	liabilities	0.0000666
2	liabilities	0.0000276
1	liability	0.0000017
2	liability	0.0000573
1	liaison	0.0000000
2	liaison	0.0000312
1	liberace	0.0000502
2	liberace	0.0000000
1	liberal	0.0000120
2	liberal	0.0003072
1	liberalization	0.0000302
2	liberalization	0.0000062
1	liberation	0.0000006
2	liberation	0.0001827
1	liberties	0.0000000
2	liberties	0.0000662
1	liberty	0.0000075
2	liberty	0.0000765
1	library	0.0000662
2	library	0.0000668
1	libya	0.0000001
2	libya	0.0000623
1	libyan	0.0000000
2	libyan	0.0000429
1	license	0.0001339
2	license	0.0000234
1	licenses	0.0000163
2	licenses	0.0000393
1	lie	0.0000004
2	lie	0.0000621
1	lied	0.0000009
2	lied	0.0000461
1	lies	0.0000407
2	lies	0.0000612
1	lieutenant	0.0000144
2	lieutenant	0.0000367
1	life	0.0004088
2	life	0.0009380
1	lifeguard	0.0000391
2	lifeguard	0.0000000
1	lifeguards	0.0000447
2	lifeguards	0.0000000
1	lifelong	0.0000030
2	lifelong	0.0000291
1	lifestyle	0.0000000
2	lifestyle	0.0000234
1	lifethreatening	0.0000178
2	lifethreatening	0.0000149
1	lifetime	0.0000213
2	lifetime	0.0000280
1	lift	0.0000629
2	lift	0.0000613
1	lifted	0.0000501
2	lifted	0.0000936
1	lifting	0.0000000
2	lifting	0.0000662
1	lifts	0.0000089
2	lifts	0.0000172
1	ligachev	0.0000000
2	ligachev	0.0000584
1	light	0.0004209
2	light	0.0001387
1	lighter	0.0000174
2	lighter	0.0000112
1	lighting	0.0000442
2	lighting	0.0000081
1	lightning	0.0000502
2	lightning	0.0000000
1	lights	0.0000482
2	lights	0.0000326
1	like	0.0012696
2	like	0.0011046
1	liked	0.0000340
2	liked	0.0000074
1	likelihood	0.0000153
2	likelihood	0.0000244
1	likely	0.0005337
2	likely	0.0003365
1	likes	0.0000121
2	likes	0.0000149
1	likud	0.0000000
2	likud	0.0000545
1	lima	0.0000107
2	lima	0.0000626
1	limbs	0.0000422
2	limbs	0.0000095
1	limit	0.0001969
2	limit	0.0001119
1	limitations	0.0000028
2	limitations	0.0000214
1	limited	0.0001938
2	limited	0.0001920
1	limiting	0.0000313
2	limiting	0.0000210
1	limits	0.0000295
2	limits	0.0001197
1	lincoln	0.0000156
2	lincoln	0.0001800
1	linda	0.0000029
2	linda	0.0000409
1	lindsay	0.0000344
2	lindsay	0.0000033
1	lindsey	0.0000000
2	lindsey	0.0000312
1	line	0.0005350
2	line	0.0002187
1	lined	0.0000226
2	lined	0.0000583
1	lines	0.0003906
2	lines	0.0001209
1	lining	0.0000243
2	lining	0.0000064
1	link	0.0000689
2	link	0.0000532
1	linked	0.0000509
2	linked	0.0001203
1	linking	0.0000099
2	linking	0.0000359
1	links	0.0000202
2	links	0.0000560
1	lip	0.0000094
2	lip	0.0000168
1	liquid	0.0000558
2	liquid	0.0000000
1	liquidated	0.0000389
2	liquidated	0.0000001
1	liquidation	0.0000558
2	liquidation	0.0000000
1	lire	0.0001451
2	lire	0.0000000
1	lisa	0.0000369
2	lisa	0.0000327
1	lisbon	0.0000104
2	lisbon	0.0000200
1	list	0.0001826
2	list	0.0002661
1	listed	0.0003483
2	listed	0.0001036
1	listen	0.0000000
2	listen	0.0000857
1	listened	0.0000096
2	listened	0.0000517
1	listening	0.0000000
2	listening	0.0000584
1	listing	0.0000329
2	listing	0.0000082
1	lists	0.0000692
2	lists	0.0000179
1	lit	0.0000335
2	lit	0.0000000
1	literally	0.0000236
2	literally	0.0000303
1	literary	0.0000000
2	literary	0.0000506
1	literature	0.0000000
2	literature	0.0000351
1	lithuania	0.0000116
2	lithuania	0.0001594
1	lithuanian	0.0000121
2	lithuanian	0.0000578
1	lithuanias	0.0000000
2	lithuanias	0.0000312
1	litigation	0.0000020
2	litigation	0.0000843
1	little	0.0007077
2	little	0.0005696
1	live	0.0003866
2	live	0.0003224
1	lived	0.0001096
2	lived	0.0001651
1	liver	0.0000033
2	liver	0.0000444
1	lives	0.0001305
2	lives	0.0003687
1	livestock	0.0001563
2	livestock	0.0000000
1	living	0.0003079
2	living	0.0003617
1	livingston	0.0000335
2	livingston	0.0000000
1	llosa	0.0000000
2	llosa	0.0000740
1	llosas	0.0000000
2	llosas	0.0000273
1	lloyd	0.0000153
2	lloyd	0.0001374
1	load	0.0000614
2	load	0.0000000
1	loaded	0.0000726
2	loaded	0.0000000
1	loading	0.0000335
2	loading	0.0000000
1	loan	0.0002704
2	loan	0.0002476
1	loans	0.0003436
2	loans	0.0000641
1	lobban	0.0000000
2	lobban	0.0000234
1	lobby	0.0000204
2	lobby	0.0000637
1	lobbying	0.0000183
2	lobbying	0.0000613
1	lobbyist	0.0000000
2	lobbyist	0.0000234
1	lobbyists	0.0000054
2	lobbyists	0.0000235
1	local	0.0004600
2	local	0.0005477
1	locally	0.0000614
2	locally	0.0000000
1	locals	0.0000558
2	locals	0.0000000
1	locate	0.0000376
2	locate	0.0000127
1	located	0.0001480
2	located	0.0000292
1	location	0.0000534
2	location	0.0000718
1	locations	0.0000811
2	locations	0.0000174
1	lock	0.0000224
2	lock	0.0000389
1	lockdown	0.0000000
2	lockdown	0.0000273
1	locked	0.0000711
2	locked	0.0000633
1	lockerbie	0.0000446
2	lockerbie	0.0000000
1	lockheed	0.0000558
2	lockheed	0.0000000
1	locks	0.0000456
2	locks	0.0000032
1	lodge	0.0000309
2	lodge	0.0000291
1	log	0.0000837
2	log	0.0000078
1	logan	0.0000233
2	logan	0.0000266
1	logical	0.0000218
2	logical	0.0000237
1	lois	0.0000295
2	lois	0.0000067
1	loma	0.0000000
2	loma	0.0000273
1	london	0.0009459
2	london	0.0001033
1	londonbased	0.0000000
2	londonbased	0.0000350
1	londons	0.0001004
2	londons	0.0000001
1	lone	0.0000000
2	lone	0.0000234
1	long	0.0007791
2	long	0.0008392
1	longawaited	0.0000175
2	longawaited	0.0000112
1	longdistance	0.0000447
2	longdistance	0.0000000
1	longer	0.0002194
2	longer	0.0002287
1	longest	0.0000173
2	longest	0.0000463
1	longrange	0.0000084
2	longrange	0.0000759
1	longstanding	0.0000000
2	longstanding	0.0000506
1	longterm	0.0001900
2	longterm	0.0000271
1	longtime	0.0000401
2	longtime	0.0001161
1	look	0.0003641
2	look	0.0003848
1	looked	0.0001639
2	looked	0.0001428
1	looking	0.0004280
2	looking	0.0002467
1	looks	0.0001721
2	looks	0.0000669
1	looming	0.0000000
2	looming	0.0000234
1	loosen	0.0000447
2	loosen	0.0000000
1	looted	0.0000334
2	looted	0.0000000
1	looters	0.0000501
2	looters	0.0000001
1	looting	0.0000447
2	looting	0.0000000
1	lopez	0.0000429
2	lopez	0.0000012
1	lord	0.0000238
2	lord	0.0000224
1	lords	0.0000000
2	lords	0.0000234
1	lorenzo	0.0000507
2	lorenzo	0.0000074
1	lorimar	0.0000214
2	lorimar	0.0000084
1	los	0.0005754
2	los	0.0002140
1	lose	0.0001168
2	lose	0.0001678
1	losers	0.0001395
2	losers	0.0000000
1	loses	0.0000333
2	loses	0.0000274
1	losing	0.0001121
2	losing	0.0001321
1	loss	0.0005721
2	loss	0.0000643
1	losses	0.0004018
2	losses	0.0000000
1	lost	0.0008132
2	lost	0.0002934
1	lot	0.0004185
2	lot	0.0004052
1	lots	0.0000563
2	lots	0.0000192
1	lottery	0.0000693
2	lottery	0.0000022
1	lou	0.0000556
2	lou	0.0000001
1	loud	0.0000557
2	loud	0.0000001
1	louis	0.0002804
2	louis	0.0000965
1	louisiana	0.0000977
2	louisiana	0.0000642
1	louisville	0.0000780
2	louisville	0.0000001
1	lounge	0.0000614
2	lounge	0.0000000
1	love	0.0000871
2	love	0.0002587
1	loved	0.0000123
2	loved	0.0000810
1	loves	0.0000005
2	loves	0.0000308
1	low	0.0005348
2	low	0.0000514
1	lowell	0.0000002
2	lowell	0.0000310
1	lower	0.0014966
2	lower	0.0000228
1	lowered	0.0000391
2	lowered	0.0000000
1	lowering	0.0000212
2	lowering	0.0000203
1	lowery	0.0000000
2	lowery	0.0000312
1	lowest	0.0002146
2	lowest	0.0000100
1	lowincome	0.0000309
2	lowincome	0.0000213
1	loyal	0.0000000
2	loyal	0.0000468
1	loyalty	0.0000000
2	loyalty	0.0000351
1	lsqb	0.0001507
2	lsqb	0.0000000
1	lt	0.0001367
2	lt	0.0002124
1	ltd	0.0001730
2	ltd	0.0000000
1	luck	0.0000539
2	luck	0.0000052
1	lucky	0.0000992
2	lucky	0.0000087
1	luis	0.0000059
2	luis	0.0000193
1	lukanov	0.0000000
2	lukanov	0.0000390
1	lukman	0.0000000
2	lukman	0.0000468
1	lunch	0.0000945
2	lunch	0.0000276
1	lundgren	0.0000000
2	lundgren	0.0000273
1	lung	0.0000162
2	lung	0.0000433
1	lungs	0.0000240
2	lungs	0.0000067
1	lure	0.0000246
2	lure	0.0000140
1	lusaka	0.0000000
2	lusaka	0.0000234
1	luther	0.0000007
2	luther	0.0000502
1	lutheran	0.0000000
2	lutheran	0.0000234
1	luxembourg	0.0000002
2	luxembourg	0.0000311
1	luxury	0.0000346
2	luxury	0.0000148
1	lying	0.0000148
2	lying	0.0000598
1	lynch	0.0000553
2	lynch	0.0000043
1	lyndon	0.0000000
2	lyndon	0.0000429
1	lynn	0.0000000
2	lynn	0.0000506
1	lynne	0.0000080
2	lynne	0.0000217
1	lyrics	0.0000000
2	lyrics	0.0000234
1	m	0.0002896
2	m	0.0002498
1	machine	0.0000544
2	machine	0.0001140
1	machinery	0.0000646
2	machinery	0.0000212
1	machines	0.0001683
2	machines	0.0000111
1	machinists	0.0000893
2	machinists	0.0000000
1	macmillan	0.0000000
2	macmillan	0.0001013
1	mad	0.0000124
2	mad	0.0000342
1	maddox	0.0000000
2	maddox	0.0000234
1	made	0.0011788
2	made	0.0018732
1	madison	0.0000243
2	madison	0.0000142
1	madrid	0.0000447
2	madrid	0.0000467
1	mafia	0.0000827
2	mafia	0.0000007
1	magazine	0.0001239
2	magazine	0.0002875
1	magazines	0.0000235
2	magazines	0.0000381
1	magellan	0.0001563
2	magellan	0.0000000
1	magellans	0.0000391
2	magellans	0.0000000
1	magic	0.0000199
2	magic	0.0000095
1	magistrate	0.0000140
2	magistrate	0.0000837
1	magistrates	0.0000000
2	magistrates	0.0000234
1	magnitude	0.0001340
2	magnitude	0.0000000
1	mail	0.0001533
2	mail	0.0000917
1	mailed	0.0000333
2	mailed	0.0000157
1	mailing	0.0000335
2	mailing	0.0000000
1	main	0.0003850
2	main	0.0003624
1	maine	0.0001615
2	maine	0.0000275
1	mainland	0.0000086
2	mainland	0.0000252
1	mainly	0.0001867
2	mainly	0.0000294
1	mainstream	0.0000136
2	mainstream	0.0000334
1	maintain	0.0000695
2	maintain	0.0002203
1	maintained	0.0000525
2	maintained	0.0000958
1	maintaining	0.0000228
2	maintaining	0.0000542
1	maintains	0.0000459
2	maintains	0.0000537
1	maintenance	0.0001790
2	maintenance	0.0000231
1	maiziere	0.0000000
2	maiziere	0.0000468
1	maj	0.0000619
2	maj	0.0000620
1	major	0.0012887
2	major	0.0006783
1	majority	0.0000345
2	majority	0.0004941
1	make	0.0011151
2	make	0.0012904
1	maker	0.0001059
2	maker	0.0000079
1	makers	0.0000998
2	makers	0.0000239
1	makes	0.0003555
2	makes	0.0001415
1	makeup	0.0000170
2	makeup	0.0000193
1	making	0.0005337
2	making	0.0003716
1	malaysia	0.0000166
2	malaysia	0.0000118
1	malcolm	0.0000000
2	malcolm	0.0000584
1	male	0.0000011
2	male	0.0001161
1	males	0.0000135
2	males	0.0000139
1	mall	0.0001018
2	mall	0.0000146
1	malls	0.0000726
2	malls	0.0000000
1	malone	0.0000558
2	malone	0.0000000
1	man	0.0006679
2	man	0.0009753
1	manage	0.0000298
2	manage	0.0000104
1	managed	0.0000618
2	managed	0.0000542
1	management	0.0007650
2	management	0.0000582
1	manager	0.0003768
2	manager	0.0001071
1	managerial	0.0000391
2	managerial	0.0000000
1	managers	0.0001727
2	managers	0.0000197
1	managing	0.0000606
2	managing	0.0000629
1	managua	0.0000000
2	managua	0.0001169
1	mandalay	0.0000335
2	mandalay	0.0000000
1	mandate	0.0000000
2	mandate	0.0000701
1	mandated	0.0000196
2	mandated	0.0000136
1	mandatory	0.0000126
2	mandatory	0.0000457
1	mandela	0.0000000
2	mandela	0.0002922
1	mandelas	0.0000000
2	mandelas	0.0000584
1	maneuvers	0.0000070
2	maneuvers	0.0000613
1	manger	0.0000015
2	manger	0.0000223
1	manhattan	0.0001146
2	manhattan	0.0000875
1	manhattans	0.0000067
2	manhattans	0.0000226
1	manila	0.0001452
2	manila	0.0000272
1	manilas	0.0000000
2	manilas	0.0000234
1	manned	0.0000837
2	manned	0.0000000
1	manner	0.0000134
2	manner	0.0000452
1	manning	0.0000058
2	manning	0.0000388
1	mans	0.0000053
2	mans	0.0000275
1	mansfield	0.0000110
2	mansfield	0.0000663
1	mansion	0.0000052
2	mansion	0.0000548
1	manslaughter	0.0000000
2	manslaughter	0.0000701
1	manuals	0.0000335
2	manuals	0.0000000
1	manuel	0.0000000
2	manuel	0.0001519
1	manufacture	0.0000272
2	manufacture	0.0000044
1	manufactured	0.0000250
2	manufactured	0.0000060
1	manufacturer	0.0000781
2	manufacturer	0.0000000
1	manufacturers	0.0002456
2	manufacturers	0.0000000
1	manufacturing	0.0003405
2	manufacturing	0.0000000
1	manville	0.0000000
2	manville	0.0000935
1	map	0.0000418
2	map	0.0000137
1	mapping	0.0000335
2	mapping	0.0000000
1	maps	0.0000558
2	maps	0.0000000
1	marathon	0.0000000
2	marathon	0.0000390
1	marble	0.0000391
2	marble	0.0000000
1	marc	0.0000279
2	marc	0.0000195
1	march	0.0007926
2	march	0.0006584
1	marched	0.0000348
2	marched	0.0000770
1	marchers	0.0000004
2	marchers	0.0000231
1	marches	0.0000000
2	marches	0.0000233
1	marching	0.0000002
2	marching	0.0000310
1	marcos	0.0000000
2	marcos	0.0001286
1	marcoses	0.0000000
2	marcoses	0.0000273
1	margaret	0.0000000
2	margaret	0.0001441
1	margin	0.0000727
2	margin	0.0001207
1	margins	0.0000333
2	margins	0.0000157
1	maria	0.0000159
2	maria	0.0000512
1	marie	0.0000327
2	marie	0.0000044
1	mariel	0.0000000
2	mariel	0.0000545
1	marietta	0.0000193
2	marietta	0.0000099
1	marijuana	0.0000452
2	marijuana	0.0000775
1	marine	0.0001849
2	marine	0.0000735
1	marines	0.0000296
2	marines	0.0000534
1	marino	0.0000251
2	marino	0.0000098
1	mario	0.0000023
2	mario	0.0000802
1	marion	0.0000328
2	marion	0.0000745
1	marisa	0.0000446
2	marisa	0.0000000
1	marital	0.0000000
2	marital	0.0000273
1	maritime	0.0000492
2	maritime	0.0000085
1	mark	0.0003655
2	mark	0.0001539
1	marked	0.0000901
2	marked	0.0000968
1	market	0.0033322
2	market	0.0000156
1	marketed	0.0000558
2	marketed	0.0000000
1	marketing	0.0002727
2	marketing	0.0000200
1	marketplace	0.0001061
2	marketplace	0.0000000
1	markets	0.0008888
2	markets	0.0000108
1	markey	0.0000000
2	markey	0.0000273
1	marking	0.0000287
2	marking	0.0000228
1	marks	0.0003474
2	marks	0.0000419
1	marlin	0.0000157
2	marlin	0.0001449
1	marriage	0.0000209
2	marriage	0.0000672
1	married	0.0000497
2	married	0.0001056
1	marrow	0.0000000
2	marrow	0.0000506
1	marry	0.0000027
2	marry	0.0000332
1	mars	0.0001682
2	mars	0.0000073
1	marshal	0.0000199
2	marshal	0.0000212
1	marshall	0.0001061
2	marshall	0.0000584
1	marshals	0.0000000
2	marshals	0.0000273
1	martial	0.0000000
2	martial	0.0000506
1	martin	0.0001726
2	martin	0.0001951
1	martinez	0.0000197
2	martinez	0.0000603
1	martyrs	0.0000002
2	martyrs	0.0000272
1	marxist	0.0000000
2	marxist	0.0000818
1	mary	0.0000674
2	mary	0.0001205
1	maryland	0.0000924
2	maryland	0.0000329
1	mash	0.0000726
2	mash	0.0000000
1	mask	0.0000226
2	mask	0.0000076
1	masked	0.0000079
2	masked	0.0000335
1	masks	0.0000205
2	masks	0.0000091
1	mason	0.0000082
2	mason	0.0000410
1	mass	0.0001460
2	mass	0.0002097
1	massachusetts	0.0000781
2	massachusetts	0.0003390
1	massacre	0.0000000
2	massacre	0.0000740
1	massacred	0.0000001
2	massacred	0.0000272
1	massage	0.0000447
2	massage	0.0000000
1	masses	0.0000049
2	masses	0.0000355
1	massive	0.0000438
2	massive	0.0001369
1	master	0.0000653
2	master	0.0000479
1	masters	0.0000439
2	masters	0.0000083
1	match	0.0000148
2	match	0.0000870
1	matched	0.0000000
2	matched	0.0000311
1	matches	0.0000148
2	matches	0.0000248
1	matching	0.0000387
2	matching	0.0000120
1	mate	0.0000075
2	mate	0.0001467
1	material	0.0001207
2	material	0.0001924
1	materials	0.0002125
2	materials	0.0000542
1	mathematical	0.0000275
2	mathematical	0.0000042
1	matta	0.0000000
2	matta	0.0000506
1	matter	0.0000930
2	matter	0.0003403
1	matters	0.0000116
2	matters	0.0001750
1	matthew	0.0000335
2	matthew	0.0000000
1	mattox	0.0000000
2	mattox	0.0000234
1	mature	0.0000396
2	mature	0.0000113
1	maturity	0.0000335
2	maturity	0.0000000
1	maung	0.0000780
2	maung	0.0000001
1	maureen	0.0000320
2	maureen	0.0000010
1	maurice	0.0000001
2	maurice	0.0000467
1	max	0.0000000
2	max	0.0000390
1	maximum	0.0001114
2	maximum	0.0000898
1	maxwell	0.0001259
2	maxwell	0.0000329
1	mayer	0.0000000
2	mayer	0.0000312
1	mayor	0.0000293
2	mayor	0.0004315
1	mayors	0.0000000
2	mayors	0.0001052
1	mazowiecki	0.0000000
2	mazowiecki	0.0000506
1	mca	0.0000860
2	mca	0.0000335
1	mccain	0.0000000
2	mccain	0.0000390
1	mccarthy	0.0000410
2	mccarthy	0.0000649
1	mccown	0.0000016
2	mccown	0.0000340
1	mcdermott	0.0000614
2	mcdermott	0.0000000
1	mcdonnell	0.0000726
2	mcdonnell	0.0000000
1	mcfarlane	0.0000000
2	mcfarlane	0.0000429
1	mcguire	0.0000558
2	mcguire	0.0000000
1	mchaffie	0.0000015
2	mchaffie	0.0000301
1	mckay	0.0000026
2	mckay	0.0000489
1	mcmahon	0.0000000
2	mcmahon	0.0000351
1	md	0.0000500
2	md	0.0000469
1	mead	0.0000000
2	mead	0.0000273
1	meager	0.0000162
2	meager	0.0000120
1	meal	0.0000726
2	meal	0.0000000
1	meals	0.0000727
2	meals	0.0000038
1	mean	0.0001214
2	mean	0.0002036
1	meaning	0.0000405
2	meaning	0.0000457
1	meaningful	0.0000065
2	meaningful	0.0000266
1	means	0.0002612
2	means	0.0002735
1	meant	0.0000748
2	meant	0.0000686
1	meantime	0.0000370
2	meantime	0.0000171
1	mears	0.0000335
2	mears	0.0000000
1	measles	0.0000000
2	measles	0.0000312
1	measure	0.0001802
2	measure	0.0004431
1	measured	0.0001272
2	measured	0.0000086
1	measurements	0.0000335
2	measurements	0.0000000
1	measures	0.0001372
2	measures	0.0002510
1	measuring	0.0000447
2	measuring	0.0000000
1	meat	0.0001757
2	meat	0.0000059
1	mecham	0.0000000
2	mecham	0.0002338
1	mechams	0.0000000
2	mechams	0.0000545
1	mechanical	0.0000446
2	mechanical	0.0000000
1	mechanism	0.0000358
2	mechanism	0.0000062
1	mechanisms	0.0000198
2	mechanisms	0.0000096
1	mechanized	0.0000332
2	mechanized	0.0000002
1	medal	0.0000000
2	medal	0.0000896
1	medals	0.0000000
2	medals	0.0000389
1	medellin	0.0000006
2	medellin	0.0001087
1	media	0.0000691
2	media	0.0003335
1	median	0.0000726
2	median	0.0000000
1	mediator	0.0000000
2	mediator	0.0000273
1	medicaid	0.0000264
2	medicaid	0.0000205
1	medical	0.0006045
2	medical	0.0003884
1	medicare	0.0000629
2	medicare	0.0000379
1	medication	0.0000057
2	medication	0.0000661
1	medicine	0.0002046
2	medicine	0.0000637
1	mediterranean	0.0000726
2	mediterranean	0.0000000
1	meese	0.0000001
2	meese	0.0003077
1	meeses	0.0000184
2	meeses	0.0000612
1	meet	0.0002034
2	meet	0.0005087
1	meeting	0.0002280
2	meeting	0.0015005
1	meetings	0.0000101
2	meetings	0.0002890
1	meets	0.0000140
2	meets	0.0000331
1	megamouth	0.0000335
2	megamouth	0.0000000
1	meir	0.0000001
2	meir	0.0000350
1	member	0.0001295
2	member	0.0009966
1	members	0.0004303
2	members	0.0019009
1	membership	0.0000000
2	membership	0.0001364
1	memo	0.0000000
2	memo	0.0000974
1	memorandum	0.0000099
2	memorandum	0.0000243
1	memorial	0.0000717
2	memorial	0.0001253
1	memories	0.0000188
2	memories	0.0000492
1	memory	0.0000700
2	memory	0.0000408
1	memphis	0.0000571
2	memphis	0.0000264
1	men	0.0005992
2	men	0.0007077
1	menem	0.0000000
2	menem	0.0000468
1	menendez	0.0000000
2	menendez	0.0000234
1	menorah	0.0000000
2	menorah	0.0000429
1	mens	0.0000276
2	mens	0.0000158
1	mental	0.0000188
2	mental	0.0000882
1	mentally	0.0000001
2	mentally	0.0000350
1	mention	0.0000009
2	mention	0.0001124
1	mentioned	0.0000260
2	mentioned	0.0001182
1	menu	0.0000391
2	menu	0.0000000
1	merc	0.0000391
2	merc	0.0000000
1	mercantile	0.0001898
2	mercantile	0.0000000
1	merchandise	0.0001172
2	merchandise	0.0000000
1	merchant	0.0000136
2	merchant	0.0000139
1	merchants	0.0000404
2	merchants	0.0000029
1	mercury	0.0000442
2	mercury	0.0000003
1	mercy	0.0000199
2	mercy	0.0000172
1	mere	0.0000223
2	mere	0.0000078
1	merely	0.0000253
2	merely	0.0000719
1	merge	0.0000000
2	merge	0.0000468
1	merged	0.0000322
2	merged	0.0000204
1	merger	0.0002646
2	merger	0.0000296
1	mergers	0.0000501
2	mergers	0.0000001
1	merit	0.0000000
2	merit	0.0000234
1	merrell	0.0000000
2	merrell	0.0000312
1	merrill	0.0000837
2	merrill	0.0000000
1	mesa	0.0000366
2	mesa	0.0000056
1	mess	0.0000164
2	mess	0.0000158
1	message	0.0000284
2	message	0.0003853
1	messages	0.0000312
2	messages	0.0000522
1	met	0.0001693
2	met	0.0006844
1	metal	0.0002227
2	metal	0.0000082
1	metals	0.0000726
2	metals	0.0000000
1	method	0.0000545
2	method	0.0000555
1	methods	0.0000952
2	methods	0.0000426
1	metric	0.0000837
2	metric	0.0000000
1	metropolitan	0.0001051
2	metropolitan	0.0000708
1	mexican	0.0000431
2	mexican	0.0001413
1	mexico	0.0003486
2	mexico	0.0002164
1	mexicos	0.0000050
2	mexicos	0.0000432
1	meyer	0.0000071
2	meyer	0.0000184
1	meyers	0.0000000
2	meyers	0.0000390
1	miami	0.0001927
2	miami	0.0001616
1	mice	0.0000726
2	mice	0.0000000
1	mich	0.0000803
2	mich	0.0000530
1	michael	0.0002165
2	michael	0.0006241
1	michaels	0.0000419
2	michaels	0.0000058
1	michel	0.0000314
2	michel	0.0000949
1	michigan	0.0001150
2	michigan	0.0000990
1	mickey	0.0000626
2	mickey	0.0000070
1	microbe	0.0000558
2	microbe	0.0000000
1	microphone	0.0000191
2	microphone	0.0000178
1	microwave	0.0000558
2	microwave	0.0000000
1	microwaves	0.0000382
2	microwaves	0.0000006
1	mid	0.0000470
2	mid	0.0000100
1	midafternoon	0.0000444
2	midafternoon	0.0000041
1	midatlantic	0.0000335
2	midatlantic	0.0000000
1	midday	0.0000924
2	midday	0.0000056
1	middle	0.0002211
2	middle	0.0004222
1	middleclass	0.0000181
2	middleclass	0.0000185
1	mideast	0.0000434
2	mideast	0.0000321
1	midland	0.0000287
2	midland	0.0000112
1	midmorning	0.0001172
2	midmorning	0.0000000
1	midnight	0.0001074
2	midnight	0.0000847
1	mids	0.0000694
2	mids	0.0000217
1	midshipman	0.0000000
2	midshipman	0.0000234
1	midway	0.0000692
2	midway	0.0000101
1	midwest	0.0001429
2	midwest	0.0000210
1	miguel	0.0000007
2	miguel	0.0000229
1	mike	0.0001338
2	mike	0.0000663
1	mikhail	0.0000018
2	mikhail	0.0003649
1	milan	0.0000416
2	milan	0.0000294
1	mile	0.0001481
2	mile	0.0000135
1	miles	0.0016732
2	miles	0.0002736
1	milestone	0.0000239
2	milestone	0.0000184
1	militant	0.0000109
2	militant	0.0000353
1	militants	0.0001432
2	militants	0.0000598
1	military	0.0003132
2	military	0.0020761
1	militarys	0.0000194
2	militarys	0.0000137
1	militia	0.0000984
2	militia	0.0000560
1	militiamen	0.0000670
2	militiamen	0.0000000
1	militias	0.0000447
2	militias	0.0000000
1	milk	0.0000813
2	milk	0.0000367
1	milken	0.0000000
2	milken	0.0001987
1	milkens	0.0000000
2	milkens	0.0000273
1	mill	0.0000347
2	mill	0.0000147
1	miller	0.0001013
2	miller	0.0000345
1	million	0.0068376
2	million	0.0013050
1	millions	0.0001635
2	millions	0.0001508
1	mills	0.0000614
2	mills	0.0000000
1	milosevic	0.0000000
2	milosevic	0.0000234
1	milstead	0.0000000
2	milstead	0.0000468
1	milton	0.0000011
2	milton	0.0000265
1	milwaukee	0.0000117
2	milwaukee	0.0000230
1	mind	0.0000953
2	mind	0.0002062
1	minds	0.0000070
2	minds	0.0000614
1	mine	0.0001290
2	mine	0.0000307
1	mineral	0.0000458
2	mineral	0.0000031
1	miners	0.0000315
2	miners	0.0001221
1	mines	0.0001343
2	mines	0.0000115
1	minimal	0.0000288
2	minimal	0.0000111
1	minimize	0.0000378
2	minimize	0.0000009
1	minimum	0.0001554
2	minimum	0.0000473
1	mining	0.0001081
2	mining	0.0000064
1	minister	0.0000640
2	minister	0.0015137
1	ministers	0.0000082
2	ministers	0.0002670
1	ministries	0.0000000
2	ministries	0.0000506
1	ministry	0.0001087
2	ministry	0.0004189
1	minn	0.0000578
2	minn	0.0000103
1	minneapolis	0.0000549
2	minneapolis	0.0000201
1	minnesota	0.0000711
2	minnesota	0.0001296
1	minnick	0.0000000
2	minnick	0.0000468
1	minor	0.0001609
2	minor	0.0000318
1	minorco	0.0000391
2	minorco	0.0000000
1	minorities	0.0000016
2	minorities	0.0000807
1	minority	0.0000012
2	minority	0.0002757
1	minus	0.0000639
2	minus	0.0000060
1	minute	0.0000566
2	minute	0.0001125
1	minutes	0.0003850
2	minutes	0.0001560
1	miracle	0.0000022
2	miracle	0.0000569
1	miranda	0.0000000
2	miranda	0.0000234
1	mirecki	0.0000000
2	mirecki	0.0000273
1	mirror	0.0000330
2	mirror	0.0000159
1	misconduct	0.0000000
2	misconduct	0.0000312
1	misdemeanor	0.0000000
2	misdemeanor	0.0000623
1	misdemeanors	0.0000002
2	misdemeanors	0.0000232
1	misleading	0.0000172
2	misleading	0.0000114
1	missed	0.0000260
2	missed	0.0000481
1	missile	0.0000851
2	missile	0.0001471
1	missiles	0.0000134
2	missiles	0.0002945
1	missing	0.0002417
2	missing	0.0000690
1	mission	0.0003399
2	mission	0.0002069
1	missions	0.0000421
2	missions	0.0000407
1	mississippi	0.0002382
2	mississippi	0.0000519
1	missouri	0.0001249
2	missouri	0.0000687
1	mistake	0.0000402
2	mistake	0.0001083
1	mistaken	0.0000000
2	mistaken	0.0000234
1	mistakenly	0.0000149
2	mistakenly	0.0000130
1	mistakes	0.0000328
2	mistakes	0.0000472
1	mistrial	0.0000000
2	mistrial	0.0000273
1	misunderstanding	0.0000126
2	misunderstanding	0.0000146
1	misuse	0.0000020
2	misuse	0.0000376
1	mitchell	0.0000174
2	mitchell	0.0001866
1	mitterrand	0.0000027
2	mitterrand	0.0000527
1	mixed	0.0002187
2	mixed	0.0000188
1	miyazawa	0.0000000
2	miyazawa	0.0000390
1	mm	0.0000837
2	mm	0.0000000
1	mo	0.0000886
2	mo	0.0000161
1	mob	0.0000000
2	mob	0.0000701
1	mobile	0.0000567
2	mobile	0.0000539
1	mobs	0.0000121
2	mobs	0.0000188
1	moche	0.0000335
2	moche	0.0000000
1	mock	0.0000254
2	mock	0.0000057
1	model	0.0001292
2	model	0.0000618
1	models	0.0001270
2	models	0.0000048
1	moderate	0.0001311
2	moderate	0.0000877
1	moderates	0.0000000
2	moderates	0.0000234
1	modern	0.0000366
2	modern	0.0000952
1	modernization	0.0000370
2	modernization	0.0000093
1	modernize	0.0000467
2	modernize	0.0000181
1	modest	0.0001422
2	modest	0.0000332
1	modified	0.0000242
2	modified	0.0000065
1	mofford	0.0000000
2	mofford	0.0000506
1	mohammad	0.0000000
2	mohammad	0.0000234
1	mohammed	0.0000359
2	mohammed	0.0000451
1	mohawk	0.0000558
2	mohawk	0.0000000
1	moines	0.0000389
2	moines	0.0000001
1	moisture	0.0000335
2	moisture	0.0000000
1	mom	0.0000194
2	mom	0.0000215
1	moment	0.0000612
2	moment	0.0001053
1	moments	0.0000303
2	moments	0.0000529
1	momentum	0.0000471
2	momentum	0.0000372
1	momma	0.0000000
2	momma	0.0000312
1	monastery	0.0000312
2	monastery	0.0000249
1	mondale	0.0000000
2	mondale	0.0000312
1	monday	0.0016445
2	monday	0.0014351
1	mondays	0.0001557
2	mondays	0.0000705
1	monet	0.0000949
2	monet	0.0000000
1	monetary	0.0001290
2	monetary	0.0000580
1	monets	0.0000502
2	monets	0.0000000
1	money	0.0010466
2	money	0.0010304
1	monia	0.0000000
2	monia	0.0000273
1	monica	0.0000168
2	monica	0.0000233
1	monieson	0.0000335
2	monieson	0.0000000
1	monitor	0.0000584
2	monitor	0.0000527
1	monitored	0.0000195
2	monitored	0.0000955
1	monitoring	0.0001537
2	monitoring	0.0000135
1	monitors	0.0000307
2	monitors	0.0000097
1	monkey	0.0000280
2	monkey	0.0000077
1	monkeys	0.0000391
2	monkeys	0.0000000
1	monks	0.0000149
2	monks	0.0000285
1	monopoly	0.0000008
2	monopoly	0.0000734
1	monoxide	0.0000670
2	monoxide	0.0000000
1	monroe	0.0000614
2	monroe	0.0000000
1	monsignor	0.0000000
2	monsignor	0.0000351
1	mont	0.0000329
2	mont	0.0000004
1	montana	0.0000911
2	montana	0.0000260
1	monte	0.0000781
2	monte	0.0000000
1	montezumas	0.0000502
2	montezumas	0.0000000
1	montgomery	0.0000236
2	montgomery	0.0000537
1	month	0.0012876
2	month	0.0009362
1	monthly	0.0002599
2	monthly	0.0000056
1	monthold	0.0000198
2	monthold	0.0000641
1	months	0.0014084
2	months	0.0007156
1	montoya	0.0000000
2	montoya	0.0000234
1	montreal	0.0000300
2	montreal	0.0000297
1	montt	0.0000000
2	montt	0.0000273
1	monument	0.0000004
2	monument	0.0000621
1	mood	0.0000296
2	mood	0.0000495
1	moon	0.0001340
2	moon	0.0000000
1	moonstruck	0.0000000
2	moonstruck	0.0000390
1	moore	0.0000001
2	moore	0.0001402
1	moral	0.0000000
2	moral	0.0001403
1	morale	0.0000392
2	morale	0.0000311
1	moratorium	0.0000000
2	moratorium	0.0000273
1	morgan	0.0000872
2	morgan	0.0000677
1	morgenstern	0.0000000
2	morgenstern	0.0000234
1	morgue	0.0000288
2	morgue	0.0000071
1	mormon	0.0000079
2	mormon	0.0000373
1	morning	0.0007380
2	morning	0.0003147
1	mornings	0.0000219
2	mornings	0.0000081
1	moroccan	0.0000101
2	moroccan	0.0000163
1	morocco	0.0000179
2	morocco	0.0000498
1	morris	0.0000633
2	morris	0.0000688
1	mortality	0.0000010
2	mortality	0.0000266
1	mortgage	0.0001588
2	mortgage	0.0000099
1	mortgages	0.0001060
2	mortgages	0.0000000
1	morton	0.0000348
2	morton	0.0000108
1	mosbacher	0.0000000
2	mosbacher	0.0000390
1	moscow	0.0000000
2	moscow	0.0006584
1	moscows	0.0000098
2	moscows	0.0000672
1	moshe	0.0000000
2	moshe	0.0000312
1	moslem	0.0001427
2	moslem	0.0001887
1	moslems	0.0000551
2	moslems	0.0000550
1	mosque	0.0000000
2	mosque	0.0000312
1	mosquito	0.0000391
2	mosquito	0.0000000
1	moss	0.0000391
2	moss	0.0000000
1	mostfavorednation	0.0000000
2	mostfavorednation	0.0000351
1	motel	0.0000192
2	motel	0.0000178
1	mother	0.0001425
2	mother	0.0004109
1	mothers	0.0000378
2	mothers	0.0001177
1	motion	0.0001211
2	motion	0.0000674
1	motions	0.0000000
2	motions	0.0000312
1	motivated	0.0000127
2	motivated	0.0000652
1	motivation	0.0000126
2	motivation	0.0000146
1	motive	0.0000611
2	motive	0.0000275
1	motives	0.0000113
2	motives	0.0000310
1	motor	0.0002847
2	motor	0.0000000
1	motorcycle	0.0000185
2	motorcycle	0.0000221
1	motorcycles	0.0000333
2	motorcycles	0.0000001
1	motorists	0.0000391
2	motorists	0.0000000
1	motors	0.0002400
2	motors	0.0000000
1	mount	0.0001138
2	mount	0.0000452
1	mountain	0.0001981
2	mountain	0.0000059
1	mountainous	0.0000390
2	mountainous	0.0000000
1	mountains	0.0001224
2	mountains	0.0000236
1	mounted	0.0000001
2	mounted	0.0000428
1	mounting	0.0000141
2	mounting	0.0000642
1	mouse	0.0000263
2	mouse	0.0000050
1	mouth	0.0000841
2	mouth	0.0000387
1	move	0.0005734
2	move	0.0004491
1	moved	0.0004675
2	moved	0.0002269
1	movement	0.0000591
2	movement	0.0005081
1	movements	0.0000622
2	movements	0.0000657
1	moves	0.0000783
2	moves	0.0001363
1	movie	0.0001062
2	movie	0.0002648
1	movies	0.0001178
2	movies	0.0000307
1	moving	0.0002854
2	moving	0.0001358
1	moynihan	0.0000001
2	moynihan	0.0000389
1	mozambique	0.0000000
2	mozambique	0.0000701
1	mpaa	0.0000391
2	mpaa	0.0000000
1	mpg	0.0000614
2	mpg	0.0000000
1	mph	0.0003684
2	mph	0.0000000
1	mr	0.0000000
2	mr	0.0003195
1	mrs	0.0000816
2	mrs	0.0015014
1	ms	0.0003438
2	ms	0.0008626
1	mubarak	0.0000000
2	mubarak	0.0000701
1	mulroney	0.0000000
2	mulroney	0.0000390
1	multibilliondollar	0.0000558
2	multibilliondollar	0.0000000
1	multimilliondollar	0.0000000
2	multimilliondollar	0.0000351
1	multinational	0.0000024
2	multinational	0.0000568
1	multiparty	0.0000000
2	multiparty	0.0000857
1	multiple	0.0000366
2	multiple	0.0000407
1	multistate	0.0000000
2	multistate	0.0000351
1	mumford	0.0000000
2	mumford	0.0000312
1	mundy	0.0000000
2	mundy	0.0000351
1	municipal	0.0001108
2	municipal	0.0000980
1	murder	0.0000252
2	murder	0.0005434
1	murdered	0.0000000
2	murdered	0.0000623
1	murderers	0.0000022
2	murderers	0.0000296
1	murdering	0.0000000
2	murdering	0.0000506
1	murders	0.0000042
2	murders	0.0000789
1	murphy	0.0000286
2	murphy	0.0001242
1	murphys	0.0000017
2	murphys	0.0000222
1	murray	0.0000369
2	murray	0.0000210
1	musburger	0.0000499
2	musburger	0.0000003
1	muscle	0.0000226
2	muscle	0.0000076
1	museum	0.0002334
2	museum	0.0000514
1	museums	0.0000401
2	museums	0.0000265
1	museveni	0.0000000
2	museveni	0.0000234
1	music	0.0001491
2	music	0.0002193
1	musical	0.0001505
2	musical	0.0000508
1	musician	0.0000000
2	musician	0.0000429
1	musicians	0.0000494
2	musicians	0.0000473
1	mussels	0.0000558
2	mussels	0.0000000
1	mutiny	0.0000000
2	mutiny	0.0000273
1	mutual	0.0000786
2	mutual	0.0000464
1	mx	0.0000000
2	mx	0.0000545
1	mydland	0.0000502
2	mydland	0.0000000
1	myers	0.0000447
2	myers	0.0000000
1	mysterious	0.0000258
2	mysterious	0.0000132
1	mystery	0.0000262
2	mystery	0.0000207
1	myth	0.0000000
2	myth	0.0000234
1	n	0.0000315
2	n	0.0001455
1	naacp	0.0000000
2	naacp	0.0000312
1	nabisco	0.0001228
2	nabisco	0.0000000
1	nablus	0.0000391
2	nablus	0.0000000
1	nadine	0.0000253
2	nadine	0.0000057
1	nagornokarabakh	0.0000000
2	nagornokarabakh	0.0000506
1	nam	0.0000391
2	nam	0.0000000
1	name	0.0001539
2	name	0.0005666
1	named	0.0002912
2	named	0.0002876
1	names	0.0001217
2	names	0.0002462
1	namibia	0.0000000
2	namibia	0.0001208
1	namibian	0.0000000
2	namibian	0.0000312
1	namphy	0.0000000
2	namphy	0.0000701
1	nancy	0.0000128
2	nancy	0.0000924
1	naomi	0.0000000
2	naomi	0.0000234
1	naples	0.0000334
2	naples	0.0000001
1	narcotics	0.0000151
2	narcotics	0.0000401
1	narez	0.0000391
2	narez	0.0000000
1	narrow	0.0000949
2	narrow	0.0000000
1	narrowed	0.0000104
2	narrowed	0.0000161
1	narrowly	0.0000268
2	narrowly	0.0000475
1	nasa	0.0003461
2	nasa	0.0000000
1	nasas	0.0000781
2	nasas	0.0000000
1	nasdaq	0.0000447
2	nasdaq	0.0000000
1	nashville	0.0000614
2	nashville	0.0000117
1	natal	0.0000061
2	natal	0.0000503
1	nation	0.0003996
2	nation	0.0005548
1	national	0.0016048
2	national	0.0020823
1	nationalist	0.0000000
2	nationalist	0.0000623
1	nationalists	0.0000000
2	nationalists	0.0000312
1	nationally	0.0000167
2	nationally	0.0000507
1	nationals	0.0000005
2	nationals	0.0000386
1	nations	0.0009822
2	nations	0.0011689
1	nationwide	0.0004013
2	nationwide	0.0001251
1	native	0.0000369
2	native	0.0001262
1	nato	0.0000000
2	nato	0.0002493
1	natos	0.0000000
2	natos	0.0000234
1	natural	0.0002541
2	natural	0.0000915
1	naturalization	0.0000000
2	naturalization	0.0000467
1	naturally	0.0000279
2	naturally	0.0000078
1	nature	0.0000796
2	nature	0.0000536
1	nauvoo	0.0000000
2	nauvoo	0.0000701
1	naval	0.0001102
2	naval	0.0000789
1	navigation	0.0000517
2	navigation	0.0000067
1	navy	0.0005697
2	navy	0.0001751
1	navys	0.0000502
2	navys	0.0000000
1	nazi	0.0000000
2	nazi	0.0001403
1	nazis	0.0000000
2	nazis	0.0000506
1	nbc	0.0004273
2	nbc	0.0000134
1	nbcs	0.0000837
2	nbcs	0.0000000
1	nc	0.0000861
2	nc	0.0000763
1	nd	0.0000575
2	nd	0.0001118
1	ne	0.0000501
2	ne	0.0000001
1	nea	0.0000000
2	nea	0.0000662
1	neal	0.0000266
2	neal	0.0000087
1	nearby	0.0002891
2	nearby	0.0001177
1	neat	0.0000221
2	neat	0.0000080
1	neb	0.0000304
2	neb	0.0000178
1	nebinger	0.0000447
2	nebinger	0.0000000
1	nebraska	0.0000690
2	nebraska	0.0000297
1	necessarily	0.0000485
2	necessarily	0.0000246
1	necessary	0.0001489
2	necessary	0.0002740
1	necessity	0.0000031
2	necessity	0.0000290
1	neck	0.0000478
2	neck	0.0000640
1	nedelin	0.0000335
2	nedelin	0.0000000
1	need	0.0003234
2	need	0.0006197
1	needed	0.0002417
2	needed	0.0003611
1	needing	0.0000158
2	needing	0.0000123
1	needles	0.0000837
2	needles	0.0000000
1	needs	0.0001122
2	needs	0.0002723
1	negative	0.0000682
2	negative	0.0001355
1	negatives	0.0000219
2	negatives	0.0000081
1	neglected	0.0000154
2	neglected	0.0000126
1	negligence	0.0000502
2	negligence	0.0000000
1	negotiate	0.0000000
2	negotiate	0.0001831
1	negotiated	0.0000540
2	negotiated	0.0000675
1	negotiating	0.0000651
2	negotiating	0.0000948
1	negotiation	0.0000000
2	negotiation	0.0000545
1	negotiations	0.0000934
2	negotiations	0.0005854
1	negotiator	0.0000117
2	negotiator	0.0000191
1	negotiators	0.0000462
2	negotiators	0.0001860
1	neighbor	0.0000790
2	neighbor	0.0000344
1	neighborhood	0.0001700
2	neighborhood	0.0000723
1	neighborhoods	0.0000646
2	neighborhoods	0.0000289
1	neighboring	0.0000497
2	neighboring	0.0000939
1	neighbors	0.0001169
2	neighbors	0.0000937
1	neil	0.0000158
2	neil	0.0000630
1	nelson	0.0000313
2	nelson	0.0001496
1	nepal	0.0000005
2	nepal	0.0000308
1	nephew	0.0000169
2	nephew	0.0000116
1	nerve	0.0000333
2	nerve	0.0000001
1	nerves	0.0000139
2	nerves	0.0000175
1	nervous	0.0000457
2	nervous	0.0000188
1	nest	0.0000613
2	nest	0.0000000
1	nesting	0.0000447
2	nesting	0.0000000
1	nestle	0.0000335
2	nestle	0.0000000
1	net	0.0002969
2	net	0.0000226
1	netherlands	0.0000614
2	netherlands	0.0000156
1	network	0.0004259
2	network	0.0001936
1	networks	0.0000064
2	networks	0.0001046
1	neutral	0.0000000
2	neutral	0.0000351
1	neutrality	0.0000000
2	neutrality	0.0000234
1	nevada	0.0001153
2	nevada	0.0000130
1	new	0.0059430
2	new	0.0036982
1	newark	0.0000551
2	newark	0.0000200
1	newest	0.0000341
2	newest	0.0000151
1	newly	0.0000318
2	newly	0.0000830
1	newmont	0.0000391
2	newmont	0.0000000
1	newport	0.0000558
2	newport	0.0000000
1	news	0.0012814
2	news	0.0017236
1	newscast	0.0000334
2	newscast	0.0000001
1	newsletter	0.0000357
2	newsletter	0.0000335
1	newspaper	0.0001991
2	newspaper	0.0007064
1	newspapers	0.0001122
2	newspapers	0.0002840
1	newsprint	0.0000447
2	newsprint	0.0000000
1	newsweek	0.0000001
2	newsweek	0.0000233
1	newt	0.0000000
2	newt	0.0000234
1	nh	0.0000410
2	nh	0.0000064
1	nicaragua	0.0000094
2	nicaragua	0.0002973
1	nicaraguan	0.0000000
2	nicaraguan	0.0001831
1	nicaraguas	0.0000000
2	nicaraguas	0.0000740
1	nice	0.0001062
2	nice	0.0000194
1	nicholas	0.0000135
2	nicholas	0.0000841
1	nicholson	0.0000357
2	nicholson	0.0000024
1	nick	0.0000173
2	nick	0.0000152
1	nickname	0.0000099
2	nickname	0.0000281
1	nicknamed	0.0000238
2	nicknamed	0.0000107
1	nicolae	0.0000000
2	nicolae	0.0000272
1	nicosia	0.0000045
2	nicosia	0.0000436
1	nicotine	0.0000558
2	nicotine	0.0000000
1	nida	0.0000558
2	nida	0.0000000
1	nielsen	0.0000526
2	nielsen	0.0000061
1	night	0.0007929
2	night	0.0008959
1	nightly	0.0000327
2	nightly	0.0000122
1	nights	0.0001202
2	nights	0.0000603
1	nih	0.0000000
2	nih	0.0000273
1	nikkei	0.0001563
2	nikkei	0.0000000
1	nikko	0.0000447
2	nikko	0.0000000
1	niklus	0.0000000
2	niklus	0.0000429
1	nikolai	0.0000000
2	nikolai	0.0000390
1	nikolais	0.0000000
2	nikolais	0.0000350
1	nine	0.0003485
2	nine	0.0002788
1	ninemonth	0.0000264
2	ninemonth	0.0000050
1	ninth	0.0000341
2	ninth	0.0000268
1	nitrogen	0.0000726
2	nitrogen	0.0000000
1	nixon	0.0000000
2	nixon	0.0001403
1	nixons	0.0000000
2	nixons	0.0000234
1	nj	0.0002296
2	nj	0.0000306
1	nm	0.0000306
2	nm	0.0000059
1	nobel	0.0000000
2	nobel	0.0000935
1	noble	0.0000102
2	noble	0.0000240
1	noboru	0.0000000
2	noboru	0.0000390
1	noise	0.0000424
2	noise	0.0000055
1	nominate	0.0000000
2	nominate	0.0000623
1	nominated	0.0000000
2	nominated	0.0001325
1	nominating	0.0000000
2	nominating	0.0000234
1	nomination	0.0000000
2	nomination	0.0003117
1	nominations	0.0000000
2	nominations	0.0000351
1	nominee	0.0000000
2	nominee	0.0002377
1	nominees	0.0000000
2	nominees	0.0000662
1	noncommunist	0.0000000
2	noncommunist	0.0000273
1	nonopec	0.0000000
2	nonopec	0.0000506
1	nonpartisan	0.0000126
2	nonpartisan	0.0000146
1	nonprofit	0.0000922
2	nonprofit	0.0000331
1	nonsmokers	0.0000447
2	nonsmokers	0.0000000
1	nonunion	0.0000391
2	nonunion	0.0000000
1	nonviolent	0.0000000
2	nonviolent	0.0000234
1	nonvoting	0.0000000
2	nonvoting	0.0000351
1	noon	0.0000821
2	noon	0.0000323
1	noontime	0.0000502
2	noontime	0.0000000
1	nordstrom	0.0001898
2	nordstrom	0.0000000
1	norfolk	0.0000146
2	norfolk	0.0000443
1	noriega	0.0000000
2	noriega	0.0003389
1	noriegas	0.0000000
2	noriegas	0.0000740
1	normal	0.0002204
2	normal	0.0000682
1	normally	0.0001710
2	normally	0.0000443
1	norman	0.0000274
2	norman	0.0000549
1	north	0.0009804
2	north	0.0009013
1	northeast	0.0002733
2	northeast	0.0000469
1	northeastern	0.0000987
2	northeastern	0.0000051
1	northern	0.0007785
2	northern	0.0001306
1	norths	0.0000000
2	norths	0.0001169
1	northwest	0.0004598
2	northwest	0.0000492
1	northwestern	0.0000735
2	northwestern	0.0000110
1	norway	0.0000027
2	norway	0.0000644
1	norwegian	0.0000335
2	norwegian	0.0000000
1	nosair	0.0000948
2	nosair	0.0000001
1	nose	0.0000735
2	nose	0.0000188
1	notably	0.0000202
2	notably	0.0000093
1	note	0.0001075
2	note	0.0001276
1	notebooks	0.0000242
2	notebooks	0.0000182
1	noted	0.0003034
2	noted	0.0002869
1	notes	0.0002107
2	notes	0.0000321
1	notice	0.0000949
2	notice	0.0001091
1	noticed	0.0000670
2	noticed	0.0000117
1	notices	0.0000291
2	notices	0.0000109
1	notification	0.0000095
2	notification	0.0000245
1	notified	0.0000875
2	notified	0.0000324
1	notify	0.0000328
2	notify	0.0000200
1	noting	0.0000928
2	noting	0.0000560
1	notion	0.0000178
2	notion	0.0000227
1	notorious	0.0000072
2	notorious	0.0000262
1	nov	0.0002368
2	nov	0.0003568
1	novel	0.0000039
2	novel	0.0000518
1	novelist	0.0000000
2	novelist	0.0000351
1	november	0.0004377
2	november	0.0002906
1	nsc	0.0000000
2	nsc	0.0000273
1	ntsb	0.0000335
2	ntsb	0.0000000
1	nuclear	0.0003297
2	nuclear	0.0003192
1	nudge	0.0000186
2	nudge	0.0000104
1	nujoma	0.0000000
2	nujoma	0.0000584
1	number	0.0011124
2	number	0.0005365
1	numbered	0.0000091
2	numbered	0.0000209
1	numbers	0.0003584
2	numbers	0.0000732
1	numerous	0.0000949
2	numerous	0.0000740
1	nun	0.0000000
2	nun	0.0000273
1	nunn	0.0000000
2	nunn	0.0001091
1	nuns	0.0000007
2	nuns	0.0000541
1	nurse	0.0000534
2	nurse	0.0000290
1	nurses	0.0001639
2	nurses	0.0000142
1	nutrition	0.0000400
2	nutrition	0.0000150
1	nutritional	0.0000329
2	nutritional	0.0000004
1	nuys	0.0000001
2	nuys	0.0000233
1	ny	0.0001790
2	ny	0.0000660
1	nyse	0.0000893
2	nyse	0.0000000
1	nyselisted	0.0000447
2	nyselisted	0.0000000
1	nyses	0.0002009
2	nyses	0.0000000
1	o	0.0000402
2	o	0.0000070
1	oak	0.0000893
2	oak	0.0000000
1	oakland	0.0000489
2	oakland	0.0000165
1	oats	0.0000829
2	oats	0.0000045
1	oau	0.0000000
2	oau	0.0000468
1	obando	0.0000000
2	obando	0.0000234
1	oberg	0.0001172
2	oberg	0.0000000
1	obey	0.0000000
2	obey	0.0000234
1	obeyed	0.0000318
2	obeyed	0.0000012
1	object	0.0000260
2	object	0.0000325
1	objected	0.0000004
2	objected	0.0000698
1	objection	0.0000000
2	objection	0.0000273
1	objections	0.0000000
2	objections	0.0000740
1	objective	0.0000246
2	objective	0.0000451
1	objectives	0.0000021
2	objectives	0.0000453
1	objects	0.0000611
2	objects	0.0000002
1	obligation	0.0000000
2	obligation	0.0000390
1	obligations	0.0000117
2	obligations	0.0000230
1	obliged	0.0000000
2	obliged	0.0000273
1	obrien	0.0000283
2	obrien	0.0000192
1	obscene	0.0000000
2	obscene	0.0000273
1	observation	0.0000564
2	observation	0.0000035
1	observatory	0.0000447
2	observatory	0.0000000
1	observe	0.0000177
2	observe	0.0000227
1	observed	0.0000114
2	observed	0.0000427
1	observer	0.0000085
2	observer	0.0000759
1	observers	0.0000554
2	observers	0.0000782
1	observing	0.0000335
2	observing	0.0000000
1	obstacle	0.0000099
2	obstacle	0.0000282
1	obstacles	0.0000092
2	obstacles	0.0000403
1	obstruction	0.0000091
2	obstruction	0.0000443
1	obtain	0.0000372
2	obtain	0.0000987
1	obtained	0.0000470
2	obtained	0.0001152
1	obtaining	0.0000135
2	obtaining	0.0000451
1	obvious	0.0000373
2	obvious	0.0000597
1	obviously	0.0000870
2	obviously	0.0001224
1	occasion	0.0000000
2	occasion	0.0000273
1	occasionally	0.0000408
2	occasionally	0.0000182
1	occasions	0.0000479
2	occasions	0.0000289
1	occidental	0.0000502
2	occidental	0.0000000
1	occupants	0.0000335
2	occupants	0.0000000
1	occupation	0.0000057
2	occupation	0.0000934
1	occupational	0.0000335
2	occupational	0.0000000
1	occupied	0.0000174
2	occupied	0.0002839
1	occupy	0.0000062
2	occupy	0.0000191
1	occupying	0.0000142
2	occupying	0.0000251
1	occur	0.0000750
2	occur	0.0000606
1	occurred	0.0003505
2	occurred	0.0001294
1	occurs	0.0000317
2	occurs	0.0000090
1	ocean	0.0000901
2	ocean	0.0000306
1	ochoa	0.0000032
2	ochoa	0.0000328
1	oconnell	0.0000000
2	oconnell	0.0000234
1	oconnor	0.0000024
2	oconnor	0.0000568
1	oct	0.0002943
2	oct	0.0003595
1	october	0.0005631
2	october	0.0001329
1	odd	0.0000178
2	odd	0.0000227
1	odds	0.0000350
2	odds	0.0000418
1	odom	0.0000377
2	odom	0.0000049
1	oferrell	0.0000000
2	oferrell	0.0000506
1	offended	0.0000000
2	offended	0.0000351
1	offenders	0.0000000
2	offenders	0.0000390
1	offense	0.0000000
2	offense	0.0000506
1	offenses	0.0000000
2	offenses	0.0000506
1	offensive	0.0000000
2	offensive	0.0001091
1	offer	0.0007973
2	offer	0.0003591
1	offered	0.0003568
2	offered	0.0002691
1	offering	0.0001662
2	offering	0.0000282
1	offers	0.0002775
2	offers	0.0000556
1	office	0.0005648
2	office	0.0016550
1	officer	0.0007567
2	officer	0.0001925
1	officers	0.0002278
2	officers	0.0005579
1	offices	0.0001675
2	offices	0.0001207
1	official	0.0004291
2	official	0.0015510
1	officially	0.0000218
2	officially	0.0001173
1	officials	0.0024175
2	officials	0.0022085
1	offset	0.0000893
2	offset	0.0000000
1	offshore	0.0000322
2	offshore	0.0000126
1	offspring	0.0000499
2	offspring	0.0000002
1	oh	0.0000242
2	oh	0.0000260
1	ohio	0.0004718
2	ohio	0.0001187
1	oil	0.0020982
2	oil	0.0001016
1	ok	0.0000441
2	ok	0.0000237
1	okla	0.0000519
2	okla	0.0000027
1	oklahoma	0.0001963
2	oklahoma	0.0000383
1	old	0.0003709
2	old	0.0003840
1	older	0.0000634
2	older	0.0000493
1	oldest	0.0000422
2	oldest	0.0000290
1	olive	0.0000454
2	olive	0.0000034
1	oliver	0.0000260
2	oliver	0.0000714
1	olympic	0.0000001
2	olympic	0.0000934
1	olympics	0.0001476
2	olympics	0.0000061
1	omaha	0.0000306
2	omaha	0.0000176
1	oman	0.0000363
2	oman	0.0000058
1	omb	0.0000000
2	omb	0.0000312
1	oneday	0.0000416
2	oneday	0.0000333
1	onefifth	0.0000131
2	onefifth	0.0000142
1	onehalf	0.0000191
2	onehalf	0.0000140
1	oneill	0.0000646
2	oneill	0.0000211
1	oneparty	0.0000000
2	oneparty	0.0000584
1	ones	0.0000915
2	ones	0.0000920
1	onethird	0.0000848
2	onethird	0.0000304
1	onetime	0.0000163
2	onetime	0.0000587
1	oneyear	0.0000533
2	oneyear	0.0000173
1	ongoing	0.0000325
2	ongoing	0.0000513
1	ontario	0.0000555
2	ontario	0.0000002
1	opec	0.0001024
2	opec	0.0000688
1	opecs	0.0000161
2	opecs	0.0000238
1	open	0.0004182
2	open	0.0004873
1	opened	0.0004647
2	opened	0.0002094
1	opening	0.0002064
2	opening	0.0002260
1	openings	0.0000211
2	openings	0.0000125
1	openly	0.0000000
2	openly	0.0000545
1	openness	0.0000000
2	openness	0.0000429
1	opens	0.0000554
2	opens	0.0000276
1	opera	0.0000293
2	opera	0.0000886
1	operate	0.0001747
2	operate	0.0000456
1	operated	0.0001036
2	operated	0.0000485
1	operates	0.0001451
2	operates	0.0000273
1	operating	0.0004648
2	operating	0.0000418
1	operation	0.0002482
2	operation	0.0002553
1	operational	0.0000430
2	operational	0.0000206
1	operations	0.0006105
2	operations	0.0001505
1	operator	0.0000935
2	operator	0.0000049
1	operators	0.0000969
2	operators	0.0000220
1	opinion	0.0000000
2	opinion	0.0002493
1	opinions	0.0000000
2	opinions	0.0000818
1	opponent	0.0000000
2	opponent	0.0001013
1	opponents	0.0000000
2	opponents	0.0002065
1	opportunities	0.0000596
2	opportunities	0.0000792
1	opportunity	0.0000939
2	opportunity	0.0001799
1	oppose	0.0000323
2	oppose	0.0001177
1	opposed	0.0000006
2	opposed	0.0002723
1	opposes	0.0000000
2	opposes	0.0001208
1	opposing	0.0000000
2	opposing	0.0000818
1	opposite	0.0000491
2	opposite	0.0000281
1	opposition	0.0000000
2	opposition	0.0009467
1	oppression	0.0000000
2	oppression	0.0000234
1	opted	0.0000238
2	opted	0.0000068
1	optimism	0.0000478
2	optimism	0.0000212
1	optimistic	0.0000412
2	optimistic	0.0000374
1	option	0.0000554
2	option	0.0000509
1	options	0.0001217
2	options	0.0000397
1	oral	0.0000125
2	oral	0.0000614
1	orange	0.0001364
2	orange	0.0000061
1	orbit	0.0001157
2	orbit	0.0000050
1	orbiter	0.0000502
2	orbiter	0.0000000
1	orchestra	0.0000000
2	orchestra	0.0000818
1	orchestras	0.0000000
2	orchestras	0.0000234
1	ordeal	0.0000095
2	ordeal	0.0000167
1	order	0.0004208
2	order	0.0006647
1	ordered	0.0002097
2	ordered	0.0005120
1	ordering	0.0000387
2	ordering	0.0000470
1	orderly	0.0000169
2	orderly	0.0000310
1	orders	0.0006255
2	orders	0.0000777
1	ordinary	0.0000273
2	ordinary	0.0000238
1	ordnance	0.0000335
2	ordnance	0.0000000
1	ore	0.0000451
2	ore	0.0000153
1	oregon	0.0001389
2	oregon	0.0000161
1	organ	0.0000120
2	organ	0.0000267
1	organic	0.0000558
2	organic	0.0000000
1	organization	0.0001740
2	organization	0.0005954
1	organizations	0.0000105
2	organizations	0.0002966
1	organize	0.0000052
2	organize	0.0000548
1	organized	0.0000366
2	organized	0.0001732
1	organizer	0.0000048
2	organizer	0.0000512
1	organizers	0.0000124
2	organizers	0.0000615
1	organizing	0.0000133
2	organizing	0.0000258
1	organs	0.0000000
2	organs	0.0000506
1	origin	0.0000285
2	origin	0.0000191
1	original	0.0001420
2	original	0.0001424
1	originally	0.0000802
2	originally	0.0000921
1	originated	0.0000335
2	originated	0.0000000
1	orion	0.0001395
2	orion	0.0000000
1	orions	0.0000335
2	orions	0.0000000
1	orleans	0.0000540
2	orleans	0.0000714
1	ornellas	0.0000502
2	ornellas	0.0000000
1	orr	0.0000000
2	orr	0.0000701
1	ortega	0.0000007
2	ortega	0.0001787
1	orthodox	0.0000001
2	orthodox	0.0000428
1	oscar	0.0000000
2	oscar	0.0000545
1	osha	0.0000502
2	osha	0.0000000
1	oswald	0.0000000
2	oswald	0.0000234
1	ottawa	0.0000101
2	ottawa	0.0000202
1	otto	0.0000053
2	otto	0.0000236
1	ought	0.0000210
2	ought	0.0001295
1	ounce	0.0004800
2	ounce	0.0000000
1	ounces	0.0000335
2	ounces	0.0000000
1	oust	0.0000009
2	oust	0.0000500
1	ousted	0.0000000
2	ousted	0.0001948
1	ouster	0.0000000
2	ouster	0.0001052
1	outage	0.0000276
2	outage	0.0000041
1	outbreak	0.0000122
2	outbreak	0.0000344
1	outcome	0.0000492
2	outcome	0.0001332
1	outdoor	0.0000158
2	outdoor	0.0000123
1	outfit	0.0000146
2	outfit	0.0000171
1	outlawed	0.0000046
2	outlawed	0.0000708
1	outlet	0.0000234
2	outlet	0.0000110
1	outlets	0.0000321
2	outlets	0.0000049
1	outline	0.0000136
2	outline	0.0000177
1	outlined	0.0000197
2	outlined	0.0000291
1	outlook	0.0001284
2	outlook	0.0000000
1	outnumbered	0.0002456
2	outnumbered	0.0000000
1	output	0.0001730
2	output	0.0000000
1	outrage	0.0000000
2	outrage	0.0000351
1	outraged	0.0000000
2	outraged	0.0000234
1	outrageous	0.0000000
2	outrageous	0.0000234
1	outreach	0.0000245
2	outreach	0.0000063
1	outright	0.0000000
2	outright	0.0000351
1	outset	0.0000001
2	outset	0.0000233
1	outside	0.0002969
2	outside	0.0006849
1	outskirts	0.0000157
2	outskirts	0.0000241
1	outspoken	0.0000061
2	outspoken	0.0000386
1	outstanding	0.0001230
2	outstanding	0.0000193
1	oval	0.0000060
2	oval	0.0000387
1	overcome	0.0000000
2	overcome	0.0000468
1	overcrowding	0.0000083
2	overcrowding	0.0000176
1	overdue	0.0000132
2	overdue	0.0000142
1	overhaul	0.0000280
2	overhaul	0.0000428
1	overhead	0.0000610
2	overhead	0.0000003
1	overlooking	0.0000446
2	overlooking	0.0000000
1	overnight	0.0002132
2	overnight	0.0000343
1	overpowered	0.0000335
2	overpowered	0.0000000
1	override	0.0000000
2	override	0.0000545
1	overruled	0.0000000
2	overruled	0.0000234
1	overrun	0.0000004
2	overrun	0.0000231
1	overseas	0.0001841
2	overseas	0.0000390
1	oversee	0.0000107
2	oversee	0.0000159
1	overseeing	0.0000144
2	overseeing	0.0000133
1	oversees	0.0000000
2	oversees	0.0000311
1	oversight	0.0000411
2	oversight	0.0000181
1	overthecounter	0.0001954
2	overthecounter	0.0000000
1	overthrow	0.0000000
2	overthrow	0.0000623
1	overthrown	0.0000000
2	overthrown	0.0000311
1	overturn	0.0000000
2	overturn	0.0000351
1	overturned	0.0000222
2	overturned	0.0000624
1	overweight	0.0000447
2	overweight	0.0000000
1	overwhelmed	0.0000230
2	overwhelmed	0.0000229
1	overwhelming	0.0000000
2	overwhelming	0.0000584
1	overwhelmingly	0.0000001
2	overwhelmingly	0.0000467
1	owe	0.0000147
2	owe	0.0000248
1	owed	0.0000388
2	owed	0.0000470
1	owen	0.0000271
2	owen	0.0001096
1	owes	0.0000412
2	owes	0.0000141
1	owned	0.0003620
2	owned	0.0000239
1	owner	0.0001640
2	owner	0.0001154
1	owners	0.0002488
2	owners	0.0000211
1	ownership	0.0001398
2	ownership	0.0000310
1	owning	0.0000415
2	owning	0.0000061
1	owns	0.0001513
2	owns	0.0000346
1	oxides	0.0000391
2	oxides	0.0000000
1	oxygen	0.0000391
2	oxygen	0.0000000
1	ozone	0.0001005
2	ozone	0.0000000
1	p	0.0000913
2	p	0.0001116
1	pa	0.0001449
2	pa	0.0000119
1	pac	0.0000000
2	pac	0.0000896
1	pace	0.0001782
2	pace	0.0000548
1	pacific	0.0004740
2	pacific	0.0000198
1	pack	0.0000380
2	pack	0.0000124
1	package	0.0000528
2	package	0.0002748
1	packages	0.0000949
2	packages	0.0000000
1	packaging	0.0001172
2	packaging	0.0000000
1	packed	0.0000085
2	packed	0.0000642
1	packing	0.0000477
2	packing	0.0000057
1	pacs	0.0000000
2	pacs	0.0000935
1	pact	0.0000288
2	pact	0.0002526
1	pacts	0.0000222
2	pacts	0.0000157
1	pad	0.0000949
2	pad	0.0000000
1	page	0.0000000
2	page	0.0001130
1	pageant	0.0001048
2	pageant	0.0000009
1	pages	0.0000038
2	pages	0.0001025
1	paid	0.0005054
2	paid	0.0002822
1	pain	0.0000600
2	pain	0.0000282
1	painful	0.0000217
2	painful	0.0000199
1	pains	0.0000092
2	pains	0.0000326
1	paint	0.0000319
2	paint	0.0000284
1	painted	0.0000164
2	painted	0.0000236
1	painter	0.0000528
2	painter	0.0000060
1	painting	0.0000702
2	painting	0.0000016
1	paintings	0.0000533
2	paintings	0.0000290
1	pair	0.0000422
2	pair	0.0000290
1	pairs	0.0000502
2	pairs	0.0000000
1	paisley	0.0000000
2	paisley	0.0000468
1	pakistan	0.0000995
2	pakistan	0.0000747
1	pakistani	0.0000099
2	pakistani	0.0000164
1	palace	0.0000067
2	palace	0.0001356
1	palestine	0.0000000
2	palestine	0.0000740
1	palestinian	0.0000000
2	palestinian	0.0003273
1	palestinians	0.0000000
2	palestinians	0.0002376
1	palm	0.0000965
2	palm	0.0000106
1	palma	0.0000000
2	palma	0.0000312
1	palmer	0.0000391
2	palmer	0.0000000
1	palmerola	0.0000447
2	palmerola	0.0000000
1	pamphlet	0.0000000
2	pamphlet	0.0000351
1	pan	0.0000852
2	pan	0.0000379
1	panama	0.0000102
2	panama	0.0004682
1	panamanian	0.0000000
2	panamanian	0.0001519
1	panamas	0.0000000
2	panamas	0.0000779
1	panel	0.0000326
2	panel	0.0004915
1	panels	0.0000353
2	panels	0.0000766
1	panetta	0.0000000
2	panetta	0.0000234
1	pang	0.0000335
2	pang	0.0000000
1	panhandle	0.0000335
2	panhandle	0.0000000
1	panic	0.0000522
2	panic	0.0000064
1	pants	0.0000142
2	pants	0.0000252
1	paper	0.0001881
2	paper	0.0001609
1	papers	0.0000119
2	papers	0.0001904
1	paperwork	0.0000366
2	paperwork	0.0000017
1	parade	0.0000199
2	parade	0.0000640
1	paralysis	0.0000002
2	paralysis	0.0000388
1	paralyzed	0.0000087
2	paralyzed	0.0000173
1	paramilitary	0.0000141
2	paramilitary	0.0000252
1	parent	0.0001641
2	parent	0.0000374
1	parental	0.0000112
2	parental	0.0000311
1	parenthood	0.0000000
2	parenthood	0.0000234
1	parents	0.0001890
2	parents	0.0004369
1	paris	0.0002356
2	paris	0.0001823
1	parish	0.0000004
2	parish	0.0000270
1	park	0.0005285
2	park	0.0001298
1	parked	0.0001005
2	parked	0.0000000
1	parker	0.0000796
2	parker	0.0000302
1	parking	0.0000654
2	parking	0.0000245
1	parkinsons	0.0000001
2	parkinsons	0.0000233
1	parks	0.0000827
2	parks	0.0000007
1	parliament	0.0000000
2	parliament	0.0003740
1	parliamentary	0.0000000
2	parliamentary	0.0001130
1	parliaments	0.0000000
2	parliaments	0.0000429
1	parole	0.0000000
2	parole	0.0001558
1	parris	0.0000000
2	parris	0.0000234
1	partial	0.0000319
2	partial	0.0000323
1	partially	0.0000447
2	partially	0.0000000
1	participants	0.0000001
2	participants	0.0000701
1	participate	0.0000219
2	participate	0.0000977
1	participated	0.0000857
2	participated	0.0000298
1	participating	0.0000384
2	participating	0.0000784
1	participation	0.0000028
2	participation	0.0000993
1	particular	0.0000723
2	particular	0.0001326
1	parties	0.0000001
2	parties	0.0005337
1	partisan	0.0000000
2	partisan	0.0000234
1	partly	0.0002164
2	partly	0.0000320
1	partner	0.0000552
2	partner	0.0000978
1	partners	0.0002016
2	partners	0.0000229
1	partnership	0.0001297
2	partnership	0.0000575
1	partnerships	0.0000422
2	partnerships	0.0000134
1	parttime	0.0000229
2	parttime	0.0000385
1	party	0.0000273
2	party	0.0023848
1	partys	0.0000000
2	partys	0.0002182
1	paso	0.0000157
2	paso	0.0000163
1	pass	0.0000965
2	pass	0.0001275
1	passage	0.0000162
2	passage	0.0001133
1	passed	0.0001164
2	passed	0.0003200
1	passenger	0.0002288
2	passenger	0.0000000
1	passengers	0.0004633
2	passengers	0.0000000
1	passes	0.0000143
2	passes	0.0000524
1	passing	0.0000303
2	passing	0.0000568
1	passion	0.0000000
2	passion	0.0000351
1	passive	0.0000355
2	passive	0.0000103
1	passport	0.0000066
2	passport	0.0000500
1	passports	0.0000080
2	passports	0.0000256
1	past	0.0007337
2	past	0.0007502
1	pastor	0.0000002
2	pastor	0.0000544
1	pat	0.0000285
2	pat	0.0000736
1	patch	0.0000781
2	patch	0.0000000
1	path	0.0000837
2	path	0.0001247
1	patience	0.0000000
2	patience	0.0000351
1	patient	0.0000534
2	patient	0.0001069
1	patients	0.0004165
2	patients	0.0000638
1	patmos	0.0000391
2	patmos	0.0000000
1	patriarca	0.0000000
2	patriarca	0.0000390
1	patricia	0.0000000
2	patricia	0.0000506
1	patrick	0.0000579
2	patrick	0.0000882
1	patrol	0.0001232
2	patrol	0.0000621
1	patrolled	0.0000234
2	patrolled	0.0000187
1	patrolman	0.0000223
2	patrolman	0.0000117
1	patrols	0.0000722
2	patrols	0.0000003
1	patronage	0.0000000
2	patronage	0.0000390
1	patrons	0.0000447
2	patrons	0.0000000
1	pattern	0.0000470
2	pattern	0.0000295
1	patterns	0.0000727
2	patterns	0.0000038
1	patterson	0.0000319
2	patterson	0.0000089
1	paul	0.0001548
2	paul	0.0004296
1	pauley	0.0000447
2	pauley	0.0000000
1	paulo	0.0000109
2	paulo	0.0000158
1	pave	0.0000000
2	pave	0.0000234
1	pavlov	0.0000335
2	pavlov	0.0000000
1	pay	0.0010104
2	pay	0.0004557
1	paychecks	0.0000140
2	paychecks	0.0000136
1	paying	0.0001667
2	paying	0.0000628
1	payless	0.0000003
2	payless	0.0000466
1	payload	0.0000391
2	payload	0.0000000
1	payment	0.0001437
2	payment	0.0000127
1	payments	0.0003090
2	payments	0.0001310
1	payne	0.0000391
2	payne	0.0000000
1	payoff	0.0000000
2	payoff	0.0000233
1	payroll	0.0000494
2	payroll	0.0000590
1	pays	0.0000701
2	pays	0.0000056
1	peabody	0.0000494
2	peabody	0.0000005
1	peace	0.0000000
2	peace	0.0009701
1	peaceful	0.0000099
2	peaceful	0.0001567
1	peacefully	0.0000110
2	peacefully	0.0000274
1	peacekeeping	0.0000109
2	peacekeeping	0.0000431
1	peacetime	0.0000150
2	peacetime	0.0000168
1	peach	0.0000071
2	peach	0.0000184
1	peak	0.0001041
2	peak	0.0000053
1	peaks	0.0000781
2	peaks	0.0000000
1	peanuts	0.0000614
2	peanuts	0.0000000
1	peasants	0.0000000
2	peasants	0.0000390
1	pedestrian	0.0000447
2	pedestrian	0.0000000
1	pedro	0.0000005
2	pedro	0.0000503
1	peer	0.0000372
2	peer	0.0000052
1	peewee	0.0000614
2	peewee	0.0000000
1	peggy	0.0000096
2	peggy	0.0000400
1	pekin	0.0000000
2	pekin	0.0000468
1	pell	0.0000000
2	pell	0.0000429
1	pen	0.0000008
2	pen	0.0000306
1	penalties	0.0000354
2	penalties	0.0000961
1	penalty	0.0000036
2	penalty	0.0002274
1	pending	0.0000605
2	pending	0.0001915
1	peninsula	0.0000359
2	peninsula	0.0000840
1	penitentiary	0.0000000
2	penitentiary	0.0000273
1	penn	0.0000111
2	penn	0.0000585
1	pennsylvania	0.0001374
2	pennsylvania	0.0000794
1	pension	0.0000015
2	pension	0.0001392
1	pentagon	0.0001698
2	pentagon	0.0003724
1	pentagons	0.0000264
2	pentagons	0.0000205
1	people	0.0034527
2	people	0.0040651
1	peoples	0.0000229
2	peoples	0.0002996
1	peoria	0.0000173
2	peoria	0.0000191
1	perceived	0.0000000
2	perceived	0.0000273
1	percent	0.0098067
2	percent	0.0007481
1	percentage	0.0003577
2	percentage	0.0000308
1	perception	0.0000250
2	perception	0.0000410
1	peres	0.0000000
2	peres	0.0001403
1	perestroika	0.0000000
2	perestroika	0.0001208
1	perez	0.0000000
2	perez	0.0000779
1	perfect	0.0000215
2	perfect	0.0000279
1	perform	0.0000639
2	perform	0.0000761
1	performance	0.0002374
2	performance	0.0001226
1	performances	0.0000574
2	performances	0.0000223
1	performed	0.0001115
2	performed	0.0000507
1	performers	0.0000000
2	performers	0.0000351
1	performing	0.0000603
2	performing	0.0000358
1	perfume	0.0000000
2	perfume	0.0000273
1	perimeter	0.0000257
2	perimeter	0.0000055
1	period	0.0005808
2	period	0.0001985
1	periodic	0.0000114
2	periodic	0.0000232
1	periods	0.0000893
2	periods	0.0000000
1	perjury	0.0000000
2	perjury	0.0000623
1	permanent	0.0000192
2	permanent	0.0001775
1	permanently	0.0000080
2	permanently	0.0000256
1	permission	0.0000412
2	permission	0.0001777
1	permit	0.0000963
2	permit	0.0001588
1	permits	0.0000799
2	permits	0.0000572
1	permitted	0.0000448
2	permitted	0.0000934
1	permitting	0.0000115
2	permitting	0.0000231
1	perry	0.0000077
2	perry	0.0000531
1	perrys	0.0000000
2	perrys	0.0000468
1	persecution	0.0000000
2	persecution	0.0000273
1	persian	0.0001138
2	persian	0.0002439
1	persistent	0.0000285
2	persistent	0.0000229
1	person	0.0002902
2	person	0.0003234
1	personal	0.0001439
2	personal	0.0003593
1	personality	0.0000000
2	personality	0.0000390
1	personally	0.0000149
2	personally	0.0000636
1	personnel	0.0001870
2	personnel	0.0001734
1	persons	0.0000377
2	persons	0.0000906
1	perspective	0.0000121
2	perspective	0.0000344
1	persuade	0.0000001
2	persuade	0.0001012
1	persuaded	0.0000074
2	persuaded	0.0000338
1	peru	0.0000004
2	peru	0.0000737
1	perus	0.0000001
2	perus	0.0000584
1	pessimism	0.0000592
2	pessimism	0.0000093
1	pesticide	0.0000837
2	pesticide	0.0000000
1	pesticides	0.0000726
2	pesticides	0.0000000
1	pests	0.0000335
2	pests	0.0000000
1	pet	0.0000399
2	pet	0.0000228
1	pete	0.0000200
2	pete	0.0000601
1	peter	0.0001441
2	peter	0.0001527
1	peters	0.0000000
2	peters	0.0000312
1	petersburg	0.0000196
2	petersburg	0.0000097
1	peterson	0.0000002
2	peterson	0.0000622
1	petition	0.0000005
2	petition	0.0001399
1	petitions	0.0000000
2	petitions	0.0000312
1	petroleum	0.0002233
2	petroleum	0.0000000
1	petty	0.0000247
2	petty	0.0000607
1	pfeiffer	0.0000000
2	pfeiffer	0.0000273
1	pharmaceutical	0.0000391
2	pharmaceutical	0.0000000
1	phase	0.0000337
2	phase	0.0000661
1	phased	0.0000002
2	phased	0.0000232
1	phil	0.0000081
2	phil	0.0000294
1	philadelphia	0.0001520
2	philadelphia	0.0000887
1	philip	0.0001103
2	philip	0.0000243
1	philippine	0.0000961
2	philippine	0.0000381
1	philippines	0.0000782
2	philippines	0.0001246
1	phillips	0.0000570
2	phillips	0.0000498
1	philosophy	0.0000105
2	philosophy	0.0000861
1	phobos	0.0000447
2	phobos	0.0000000
1	phoenix	0.0001005
2	phoenix	0.0000078
1	phone	0.0000641
2	phone	0.0001267
1	photo	0.0000080
2	photo	0.0000372
1	photograph	0.0000392
2	photograph	0.0000350
1	photographer	0.0000019
2	photographer	0.0000610
1	photographers	0.0000001
2	photographers	0.0000622
1	photographs	0.0000402
2	photographs	0.0000655
1	photos	0.0000367
2	photos	0.0000133
1	phrase	0.0000257
2	phrase	0.0000444
1	phyllis	0.0000000
2	phyllis	0.0000351
1	physical	0.0000518
2	physical	0.0000885
1	physically	0.0000226
2	physically	0.0000193
1	physician	0.0000223
2	physician	0.0000468
1	physicians	0.0000004
2	physicians	0.0000777
1	piano	0.0000449
2	piano	0.0000271
1	pick	0.0001132
2	pick	0.0000456
1	picked	0.0001454
2	picked	0.0001089
1	picket	0.0000614
2	picket	0.0000000
1	picking	0.0000618
2	picking	0.0000153
1	picks	0.0000470
2	picks	0.0000062
1	pickup	0.0000814
2	pickup	0.0000094
1	picture	0.0001167
2	picture	0.0001055
1	pictures	0.0001918
2	pictures	0.0000492
1	pie	0.0000502
2	pie	0.0000000
1	piece	0.0000925
2	piece	0.0000406
1	pieces	0.0001357
2	pieces	0.0000650
1	pierre	0.0000497
2	pierre	0.0000432
1	pig	0.0000355
2	pig	0.0000064
1	pigeon	0.0000000
2	pigeon	0.0000312
1	pigs	0.0000331
2	pigs	0.0000003
1	pile	0.0000436
2	pile	0.0000046
1	pilgrimage	0.0000216
2	pilgrimage	0.0000083
1	pilgrims	0.0000146
2	pilgrims	0.0000326
1	pills	0.0000111
2	pills	0.0000195
1	pillsbury	0.0000726
2	pillsbury	0.0000000
1	pillsburys	0.0000335
2	pillsburys	0.0000000
1	pilot	0.0004149
2	pilot	0.0000143
1	pilots	0.0005247
2	pilots	0.0000000
1	pine	0.0000335
2	pine	0.0000000
1	pink	0.0000004
2	pink	0.0000270
1	pinnacle	0.0000502
2	pinnacle	0.0000000
1	pinochet	0.0000000
2	pinochet	0.0000351
1	pioneer	0.0000355
2	pioneer	0.0000142
1	pioneered	0.0000335
2	pioneered	0.0000000
1	pipe	0.0000670
2	pipe	0.0000000
1	pipeline	0.0001240
2	pipeline	0.0000069
1	pipelines	0.0000153
2	pipelines	0.0000127
1	pipes	0.0000447
2	pipes	0.0000000
1	pistol	0.0000297
2	pistol	0.0000299
1	pit	0.0000335
2	pit	0.0000000
1	pitch	0.0000079
2	pitch	0.0000218
1	pits	0.0000402
2	pits	0.0000070
1	pittsburgh	0.0001166
2	pittsburgh	0.0000706
1	pittston	0.0000010
2	pittston	0.0000383
1	pizza	0.0000001
2	pizza	0.0000311
1	place	0.0004433
2	place	0.0005866
1	placed	0.0001449
2	placed	0.0001755
1	placement	0.0000335
2	placement	0.0000000
1	places	0.0000660
2	places	0.0000981
1	placing	0.0000518
2	placing	0.0000184
1	plagued	0.0000179
2	plagued	0.0000148
1	plain	0.0000613
2	plain	0.0000351
1	plainclothes	0.0000000
2	plainclothes	0.0000312
1	plains	0.0001674
2	plains	0.0000000
1	plaintiffs	0.0000014
2	plaintiffs	0.0000808
1	plan	0.0008608
2	plan	0.0010237
1	plane	0.0007816
2	plane	0.0000856
1	planes	0.0005348
2	planes	0.0000124
1	planet	0.0001674
2	planet	0.0000000
1	planetary	0.0000335
2	planetary	0.0000000
1	planets	0.0000391
2	planets	0.0000000
1	plank	0.0000000
2	plank	0.0000351
1	planks	0.0000000
2	planks	0.0000351
1	planned	0.0003565
2	planned	0.0004836
1	planners	0.0000117
2	planners	0.0000191
1	planning	0.0001191
2	planning	0.0001779
1	plans	0.0005344
2	plans	0.0004958
1	plant	0.0010688
2	plant	0.0000020
1	planted	0.0000837
2	planted	0.0000000
1	planting	0.0000676
2	planting	0.0000035
1	plants	0.0004909
2	plants	0.0000041
1	plastic	0.0001544
2	plastic	0.0000286
1	plastics	0.0000305
2	plastics	0.0000099
1	plate	0.0000357
2	plate	0.0000063
1	plates	0.0000893
2	plates	0.0000000
1	platform	0.0000334
2	platform	0.0002221
1	platforms	0.0000402
2	platforms	0.0000070
1	platoon	0.0000000
2	platoon	0.0000234
1	play	0.0000955
2	play	0.0002684
1	playboy	0.0000000
2	playboy	0.0000312
1	played	0.0000580
2	played	0.0002439
1	player	0.0000001
2	player	0.0000662
1	players	0.0000981
2	players	0.0000601
1	playing	0.0000517
2	playing	0.0001821
1	plays	0.0000182
2	plays	0.0000535
1	plaza	0.0000342
2	plaza	0.0000190
1	plc	0.0001395
2	plc	0.0000000
1	plea	0.0000114
2	plea	0.0001713
1	plead	0.0000000
2	plead	0.0000429
1	pleaded	0.0000010
2	pleaded	0.0002915
1	pleading	0.0000000
2	pleading	0.0000272
1	pleas	0.0000125
2	pleas	0.0000419
1	pleasant	0.0000223
2	pleasant	0.0000078
1	please	0.0000147
2	please	0.0000209
1	pleased	0.0000363
2	pleased	0.0000954
1	pleasure	0.0000360
2	pleasure	0.0000177
1	pledge	0.0000099
2	pledge	0.0001333
1	pledged	0.0000380
2	pledged	0.0001176
1	pledges	0.0000000
2	pledges	0.0000234
1	plenty	0.0000458
2	plenty	0.0000421
1	plessey	0.0000335
2	plessey	0.0000000
1	plight	0.0000102
2	plight	0.0000280
1	plo	0.0000000
2	plo	0.0002338
1	plot	0.0000194
2	plot	0.0000955
1	plotted	0.0000000
2	plotted	0.0000234
1	plotting	0.0000000
2	plotting	0.0000351
1	plummeted	0.0000469
2	plummeted	0.0000024
1	plunge	0.0000967
2	plunge	0.0000065
1	plunged	0.0001323
2	plunged	0.0000089
1	plunging	0.0000335
2	plunging	0.0000000
1	plus	0.0001285
2	plus	0.0000467
1	plutonium	0.0000335
2	plutonium	0.0000000
1	pm	0.0006591
2	pm	0.0000737
1	pneumonia	0.0000373
2	pneumonia	0.0000090
1	pocket	0.0000529
2	pocket	0.0000176
1	pockets	0.0000305
2	pockets	0.0000255
1	podium	0.0000000
2	podium	0.0000351
1	poe	0.0000000
2	poe	0.0000312
1	poem	0.0000458
2	poem	0.0000148
1	poems	0.0000036
2	poems	0.0000325
1	poet	0.0000000
2	poet	0.0000467
1	poetry	0.0000000
2	poetry	0.0000312
1	poindexter	0.0000000
2	poindexter	0.0000857
1	point	0.0008315
2	point	0.0003079
1	pointed	0.0000409
2	pointed	0.0001000
1	pointing	0.0000533
2	pointing	0.0000212
1	points	0.0010512
2	points	0.0001351
1	poison	0.0000412
2	poison	0.0000492
1	poisoning	0.0000543
2	poisoning	0.0000010
1	poland	0.0000000
2	poland	0.0001870
1	polands	0.0000000
2	polands	0.0000584
1	polaroid	0.0000000
2	polaroid	0.0000623
1	pole	0.0000335
2	pole	0.0000000
1	poles	0.0000125
2	poles	0.0000497
1	police	0.0023268
2	police	0.0025991
1	policeman	0.0000274
2	policeman	0.0000977
1	policemen	0.0000330
2	policemen	0.0001289
1	policies	0.0000962
2	policies	0.0003536
1	policy	0.0002314
2	policy	0.0008709
1	policymaking	0.0000000
2	policymaking	0.0000312
1	polisario	0.0000000
2	polisario	0.0000390
1	polish	0.0000000
2	polish	0.0001441
1	politburo	0.0000000
2	politburo	0.0001948
1	political	0.0000351
2	political	0.0020832
1	politically	0.0000000
2	politically	0.0001130
1	politician	0.0000000
2	politician	0.0000273
1	politicians	0.0000000
2	politicians	0.0001870
1	politics	0.0000000
2	politics	0.0002883
1	polk	0.0000335
2	polk	0.0000000
1	poll	0.0000000
2	poll	0.0004091
1	polling	0.0000000
2	polling	0.0000312
1	polls	0.0000000
2	polls	0.0002415
1	pollutants	0.0000613
2	pollutants	0.0000001
1	pollution	0.0002824
2	pollution	0.0000133
1	polly	0.0000001
2	polly	0.0000311
1	pomona	0.0000447
2	pomona	0.0000000
1	pont	0.0000236
2	pont	0.0000225
1	pontiff	0.0000000
2	pontiff	0.0000545
1	pool	0.0000650
2	pool	0.0000481
1	poor	0.0002615
2	poor	0.0002694
1	poorest	0.0000453
2	poorest	0.0000112
1	poorly	0.0000324
2	poorly	0.0000125
1	poors	0.0000447
2	poors	0.0000000
1	pop	0.0000329
2	pop	0.0000277
1	pope	0.0000000
2	pope	0.0002961
1	popes	0.0000000
2	popes	0.0000312
1	popeye	0.0000558
2	popeye	0.0000000
1	popov	0.0000000
2	popov	0.0000584
1	popular	0.0000998
2	popular	0.0003004
1	popularity	0.0000521
2	popularity	0.0000377
1	populated	0.0000325
2	populated	0.0000007
1	population	0.0003713
2	population	0.0002239
1	populations	0.0000557
2	populations	0.0000001
1	populist	0.0000000
2	populist	0.0000312
1	pork	0.0001741
2	pork	0.0000148
1	pornographic	0.0000084
2	pornographic	0.0000253
1	pornography	0.0000013
2	pornography	0.0000303
1	port	0.0002402
2	port	0.0000934
1	portable	0.0000363
2	portable	0.0000136
1	portauprince	0.0000000
2	portauprince	0.0000234
1	porter	0.0000725
2	porter	0.0000000
1	portfolio	0.0000725
2	portfolio	0.0000000
1	portion	0.0001489
2	portion	0.0000324
1	portions	0.0000335
2	portions	0.0000312
1	portland	0.0001097
2	portland	0.0000286
1	portrait	0.0000218
2	portrait	0.0000198
1	portrayed	0.0000207
2	portrayed	0.0000518
1	portugal	0.0000084
2	portugal	0.0000409
1	portuguese	0.0000000
2	portuguese	0.0000273
1	pose	0.0000256
2	pose	0.0000601
1	posed	0.0000408
2	posed	0.0000456
1	poses	0.0000186
2	poses	0.0000143
1	posing	0.0000063
2	posing	0.0000268
1	position	0.0002446
2	position	0.0004409
1	positions	0.0001199
2	positions	0.0001657
1	positive	0.0001744
2	positive	0.0001120
1	positively	0.0000052
2	positively	0.0000197
1	possession	0.0000064
2	possession	0.0001241
1	possessions	0.0000239
2	possessions	0.0000223
1	possibility	0.0001278
2	possibility	0.0001757
1	possible	0.0003243
2	possible	0.0005645
1	possibly	0.0001297
2	possibly	0.0000887
1	post	0.0000556
2	post	0.0003781
1	postal	0.0001435
2	postal	0.0000089
1	posted	0.0002009
2	posted	0.0000000
1	posters	0.0000000
2	posters	0.0000312
1	posting	0.0000391
2	posting	0.0000000
1	postponed	0.0000362
2	postponed	0.0000488
1	posts	0.0000199
2	posts	0.0000485
1	postwar	0.0000000
2	postwar	0.0000584
1	potato	0.0000391
2	potato	0.0000000
1	potatoes	0.0000558
2	potatoes	0.0000000
1	potential	0.0002125
2	potential	0.0002491
1	potentially	0.0000681
2	potentially	0.0000265
1	poultry	0.0000558
2	poultry	0.0000000
1	pound	0.0005023
2	pound	0.0000000
1	pounds	0.0004689
2	pounds	0.0000000
1	poured	0.0000132
2	poured	0.0000142
1	poverty	0.0000000
2	poverty	0.0000896
1	powell	0.0000000
2	powell	0.0000623
1	power	0.0006325
2	power	0.0007935
1	powerful	0.0000634
2	powerful	0.0001661
1	powers	0.0000000
2	powers	0.0001909
1	practical	0.0000173
2	practical	0.0000463
1	practically	0.0000146
2	practically	0.0000210
1	practice	0.0000646
2	practice	0.0001653
1	practiced	0.0000032
2	practiced	0.0000250
1	practices	0.0001233
2	practices	0.0000971
1	practicing	0.0000157
2	practicing	0.0000202
1	prague	0.0000000
2	prague	0.0000974
1	praise	0.0000001
2	praise	0.0000584
1	praised	0.0000000
2	praised	0.0001364
1	pray	0.0000000
2	pray	0.0000234
1	prayer	0.0000000
2	prayer	0.0001013
1	prayers	0.0000082
2	prayers	0.0000332
1	praying	0.0000000
2	praying	0.0000273
1	preacher	0.0000000
2	preacher	0.0000234
1	precautionary	0.0000330
2	precautionary	0.0000003
1	preceded	0.0000300
2	preceded	0.0000414
1	precincts	0.0000000
2	precincts	0.0000234
1	precious	0.0000558
2	precious	0.0000000
1	precise	0.0000300
2	precise	0.0000180
1	precisely	0.0000183
2	precisely	0.0000223
1	predawn	0.0000160
2	predawn	0.0000278
1	predecessor	0.0000000
2	predecessor	0.0000468
1	predict	0.0000493
2	predict	0.0000240
1	predicted	0.0002078
2	predicted	0.0001082
1	predicting	0.0000419
2	predicting	0.0000019
1	prediction	0.0000301
2	prediction	0.0000062
1	predictions	0.0000466
2	predictions	0.0000064
1	predominantly	0.0000569
2	predominantly	0.0000888
1	prefer	0.0000228
2	prefer	0.0000503
1	preference	0.0000058
2	preference	0.0000310
1	preferred	0.0000448
2	preferred	0.0000467
1	pregnancy	0.0000000
2	pregnancy	0.0000584
1	pregnant	0.0000313
2	pregnant	0.0000911
1	preliminary	0.0000975
2	preliminary	0.0000878
1	premature	0.0000619
2	premature	0.0000191
1	premier	0.0000390
2	premier	0.0001598
1	premium	0.0001284
2	premium	0.0000000
1	premiums	0.0000391
2	premiums	0.0000000
1	prenatal	0.0000000
2	prenatal	0.0000312
1	prensa	0.0000154
2	prensa	0.0000204
1	preparation	0.0000188
2	preparation	0.0000103
1	preparations	0.0000283
2	preparations	0.0000465
1	prepare	0.0000756
2	prepare	0.0000953
1	prepared	0.0001187
2	prepared	0.0002678
1	preparing	0.0000913
2	preparing	0.0001116
1	prescription	0.0000220
2	prescription	0.0000119
1	presence	0.0000639
2	presence	0.0001853
1	present	0.0000751
2	present	0.0003995
1	presentation	0.0000123
2	presentation	0.0000226
1	presented	0.0000375
2	presented	0.0001842
1	presents	0.0000208
2	presents	0.0000245
1	preservation	0.0000300
2	preservation	0.0000063
1	preserve	0.0000005
2	preserve	0.0000425
1	preserved	0.0000389
2	preserved	0.0000001
1	presidency	0.0000000
2	presidency	0.0001870
1	president	0.0012533
2	president	0.0048873
1	presidentelect	0.0000154
2	presidentelect	0.0000438
1	presidential	0.0000000
2	presidential	0.0010441
1	presidents	0.0000000
2	presidents	0.0003195
1	presiding	0.0000000
2	presiding	0.0000312
1	presley	0.0001005
2	presley	0.0000000
1	presleys	0.0000391
2	presleys	0.0000000
1	press	0.0002130
2	press	0.0008098
1	pressed	0.0000143
2	pressed	0.0000718
1	presser	0.0000000
2	presser	0.0000234
1	pressing	0.0000112
2	pressing	0.0000662
1	pressure	0.0002935
2	pressure	0.0002782
1	pressured	0.0000614
2	pressured	0.0000000
1	pressures	0.0000636
2	pressures	0.0000336
1	prestige	0.0000190
2	prestige	0.0000296
1	presumably	0.0000067
2	presumably	0.0000382
1	presumed	0.0000523
2	presumed	0.0000297
1	presumption	0.0000000
2	presumption	0.0000273
1	pretoria	0.0000000
2	pretoria	0.0000312
1	pretrial	0.0000000
2	pretrial	0.0000234
1	pretty	0.0001838
2	pretty	0.0001405
1	prevail	0.0000128
2	prevail	0.0000339
1	prevailed	0.0000351
2	prevailed	0.0000106
1	prevent	0.0001917
2	prevent	0.0001817
1	prevented	0.0000203
2	prevented	0.0000988
1	preventing	0.0000295
2	preventing	0.0000261
1	prevention	0.0000730
2	prevention	0.0000075
1	previous	0.0007395
2	previous	0.0001267
1	previously	0.0002029
2	previously	0.0001116
1	price	0.0016784
2	price	0.0000324
1	priceless	0.0000391
2	priceless	0.0000000
1	prices	0.0026401
2	prices	0.0000000
1	pride	0.0000348
2	pride	0.0000224
1	priest	0.0000000
2	priest	0.0000935
1	priests	0.0000000
2	priests	0.0000584
1	primaries	0.0000000
2	primaries	0.0000818
1	primarily	0.0000970
2	primarily	0.0000492
1	primary	0.0000875
2	primary	0.0004960
1	prime	0.0002199
2	prime	0.0006647
1	primetime	0.0000499
2	primetime	0.0000042
1	primitive	0.0000277
2	primitive	0.0000041
1	prince	0.0000537
2	prince	0.0001106
1	princess	0.0000108
2	princess	0.0000509
1	princeton	0.0000001
2	princeton	0.0000272
1	principal	0.0001175
2	principal	0.0000544
1	principle	0.0000128
2	principle	0.0000885
1	principles	0.0000096
2	principles	0.0000323
1	pring	0.0000000
2	pring	0.0000273
1	print	0.0000175
2	print	0.0000423
1	printed	0.0000035
2	printed	0.0000443
1	prior	0.0000642
2	prior	0.0000993
1	priorities	0.0000171
2	priorities	0.0000816
1	priority	0.0000639
2	priority	0.0000606
1	prison	0.0000002
2	prison	0.0011102
1	prisoner	0.0000000
2	prisoner	0.0000662
1	prisoners	0.0000007
2	prisoners	0.0003424
1	prisons	0.0000000
2	prisons	0.0000662
1	privacy	0.0000000
2	privacy	0.0000545
1	private	0.0005056
2	private	0.0005354
1	privately	0.0000860
2	privately	0.0000374
1	privileges	0.0000078
2	privileges	0.0000179
1	prize	0.0000683
2	prize	0.0001899
1	probability	0.0000223
2	probability	0.0000078
1	probable	0.0000334
2	probable	0.0000117
1	probate	0.0000000
2	probate	0.0000273
1	probation	0.0000010
2	probation	0.0000538
1	probe	0.0000931
2	probe	0.0000987
1	probes	0.0000335
2	probes	0.0000000
1	problem	0.0005529
2	problem	0.0003348
1	problems	0.0007129
2	problems	0.0005543
1	procedural	0.0000000
2	procedural	0.0000234
1	procedure	0.0000696
2	procedure	0.0000449
1	procedures	0.0001134
2	procedures	0.0000572
1	proceed	0.0000596
2	proceed	0.0000402
1	proceeding	0.0000433
2	proceeding	0.0000127
1	proceedings	0.0000002
2	proceedings	0.0001128
1	proceeds	0.0000616
2	proceeds	0.0000233
1	process	0.0001809
2	process	0.0003958
1	processed	0.0000502
2	processed	0.0000000
1	processes	0.0000165
2	processes	0.0000236
1	processing	0.0001172
2	processing	0.0000000
1	procession	0.0000334
2	procession	0.0000000
1	prochoice	0.0000000
2	prochoice	0.0000312
1	proclaim	0.0000116
2	proclaim	0.0000153
1	proclaimed	0.0000004
2	proclaimed	0.0000348
1	procurement	0.0000092
2	procurement	0.0000598
1	prodemocracy	0.0000000
2	prodemocracy	0.0001208
1	produce	0.0002585
2	produce	0.0000962
1	produced	0.0003164
2	produced	0.0000636
1	producer	0.0002244
2	producer	0.0000070
1	producers	0.0002117
2	producers	0.0000042
1	produces	0.0000670
2	produces	0.0000000
1	producing	0.0001190
2	producing	0.0000026
1	product	0.0003756
2	product	0.0000144
1	production	0.0009859
2	production	0.0000404
1	productions	0.0000258
2	productions	0.0000171
1	productive	0.0000061
2	productive	0.0000464
1	productivity	0.0000510
2	productivity	0.0000073
1	products	0.0008438
2	products	0.0000460
1	profession	0.0000116
2	profession	0.0000153
1	professional	0.0001038
2	professional	0.0000756
1	professionals	0.0000359
2	professionals	0.0000295
1	professor	0.0001054
2	professor	0.0002186
1	professors	0.0000000
2	professors	0.0000351
1	profile	0.0000187
2	profile	0.0000337
1	profit	0.0002975
2	profit	0.0000183
1	profitable	0.0001004
2	profitable	0.0000000
1	profits	0.0002993
2	profits	0.0001027
1	profittaking	0.0000558
2	profittaking	0.0000000
1	profound	0.0000118
2	profound	0.0000190
1	progovernment	0.0000003
2	progovernment	0.0000271
1	program	0.0011567
2	program	0.0008874
1	programming	0.0000579
2	programming	0.0000063
1	programs	0.0003018
2	programs	0.0004205
1	progress	0.0001377
2	progress	0.0003052
1	progressive	0.0000000
2	progressive	0.0000468
1	prohibit	0.0000145
2	prohibit	0.0000444
1	prohibited	0.0000003
2	prohibited	0.0000505
1	prohibits	0.0000000
2	prohibits	0.0000351
1	proiranian	0.0000231
2	proiranian	0.0000189
1	project	0.0004581
2	project	0.0001516
1	projected	0.0001228
2	projected	0.0000000
1	projection	0.0000391
2	projection	0.0000000
1	projections	0.0001003
2	projections	0.0000001
1	projects	0.0001012
2	projects	0.0001592
1	prolonged	0.0000186
2	prolonged	0.0000143
1	prom	0.0000000
2	prom	0.0000312
1	prominent	0.0000287
2	prominent	0.0001553
1	promise	0.0000000
2	promise	0.0001714
1	promised	0.0000356
2	promised	0.0003063
1	promises	0.0000028
2	promises	0.0000799
1	promising	0.0000236
2	promising	0.0000342
1	promote	0.0000325
2	promote	0.0000903
1	promoted	0.0000065
2	promoted	0.0000500
1	promotes	0.0000176
2	promotes	0.0000111
1	promoting	0.0000127
2	promoting	0.0000496
1	promotion	0.0000443
2	promotion	0.0000470
1	promotions	0.0000208
2	promotions	0.0000206
1	prompt	0.0000327
2	prompt	0.0000240
1	prompted	0.0001308
2	prompted	0.0000918
1	prompting	0.0000521
2	prompting	0.0000143
1	promptly	0.0000240
2	promptly	0.0000144
1	pronounced	0.0000554
2	pronounced	0.0000237
1	proof	0.0000394
2	proof	0.0000894
1	propaganda	0.0000000
2	propaganda	0.0000312
1	propelled	0.0000115
2	propelled	0.0000154
1	proper	0.0000700
2	proper	0.0000603
1	properly	0.0001028
2	properly	0.0000218
1	properties	0.0000248
2	properties	0.0000489
1	property	0.0002288
2	property	0.0002689
1	proponents	0.0000303
2	proponents	0.0000139
1	proposal	0.0001767
2	proposal	0.0005818
1	proposals	0.0000781
2	proposals	0.0002299
1	propose	0.0000312
2	propose	0.0000562
1	proposed	0.0003240
2	proposed	0.0004751
1	proposing	0.0000079
2	proposing	0.0000296
1	proposition	0.0000000
2	proposition	0.0000273
1	propped	0.0000079
2	propped	0.0000178
1	propulsion	0.0000391
2	propulsion	0.0000000
1	prosecute	0.0000004
2	prosecute	0.0000348
1	prosecuted	0.0000007
2	prosecuted	0.0000385
1	prosecution	0.0000000
2	prosecution	0.0001597
1	prosecutor	0.0000000
2	prosecutor	0.0002299
1	prosecutors	0.0000000
2	prosecutors	0.0003740
1	prospect	0.0000560
2	prospect	0.0000505
1	prospective	0.0000079
2	prospective	0.0000490
1	prospects	0.0000513
2	prospects	0.0000850
1	prosperity	0.0000000
2	prosperity	0.0001013
1	prostitute	0.0000077
2	prostitute	0.0000375
1	prostitutes	0.0000000
2	prostitutes	0.0000350
1	protect	0.0000889
2	protect	0.0002925
1	protected	0.0000520
2	protected	0.0000650
1	protecting	0.0000000
2	protecting	0.0000935
1	protection	0.0003855
2	protection	0.0001711
1	protectionist	0.0000181
2	protectionist	0.0000302
1	protective	0.0000519
2	protective	0.0000183
1	protects	0.0000373
2	protects	0.0000012
1	protein	0.0000445
2	protein	0.0000001
1	protest	0.0000257
2	protest	0.0004301
1	protestant	0.0000115
2	protestant	0.0000582
1	protested	0.0000000
2	protested	0.0000857
1	protesters	0.0000143
2	protesters	0.0002783
1	protesting	0.0000061
2	protesting	0.0000854
1	protests	0.0000074
2	protests	0.0002286
1	protocol	0.0000000
2	protocol	0.0000429
1	proud	0.0000080
2	proud	0.0000606
1	prove	0.0000380
2	prove	0.0001566
1	proved	0.0000400
2	proved	0.0000539
1	proven	0.0000618
2	proven	0.0000192
1	proves	0.0000042
2	proves	0.0000282
1	provide	0.0003259
2	provide	0.0004193
1	provided	0.0002206
2	provided	0.0001850
1	providence	0.0000077
2	providence	0.0000297
1	provides	0.0001397
2	provides	0.0001128
1	providing	0.0000378
2	providing	0.0001606
1	province	0.0001150
2	province	0.0001574
1	provinces	0.0000095
2	provinces	0.0000908
1	provincial	0.0000079
2	provincial	0.0000412
1	proving	0.0000073
2	proving	0.0000222
1	provision	0.0000703
2	provision	0.0000951
1	provisions	0.0000281
2	provisions	0.0001791
1	provocation	0.0000036
2	provocation	0.0000208
1	provoked	0.0000185
2	provoked	0.0000221
1	proxmire	0.0000000
2	proxmire	0.0000234
1	proxy	0.0000374
2	proxy	0.0000051
1	prudent	0.0000163
2	prudent	0.0000120
1	prudentialbache	0.0000335
2	prudentialbache	0.0000000
1	psychiatric	0.0000000
2	psychiatric	0.0000428
1	psychiatrist	0.0000061
2	psychiatrist	0.0000191
1	psychological	0.0000875
2	psychological	0.0000169
1	psychologist	0.0000240
2	psychologist	0.0000222
1	ptl	0.0000000
2	ptl	0.0000623
1	public	0.0006446
2	public	0.0013968
1	publication	0.0000855
2	publication	0.0000572
1	publications	0.0000034
2	publications	0.0000483
1	publicist	0.0000128
2	publicist	0.0000300
1	publicity	0.0000182
2	publicity	0.0001003
1	publicize	0.0000125
2	publicize	0.0000186
1	publicized	0.0000149
2	publicized	0.0000246
1	publicly	0.0000370
2	publicly	0.0001261
1	publish	0.0000001
2	publish	0.0000467
1	published	0.0001186
2	published	0.0003224
1	publisher	0.0000487
2	publisher	0.0000829
1	publishers	0.0000000
2	publishers	0.0000468
1	publishing	0.0000736
2	publishing	0.0000421
1	puerto	0.0000403
2	puerto	0.0000732
1	puette	0.0000391
2	puette	0.0000000
1	pulitzer	0.0000009
2	pulitzer	0.0000345
1	pull	0.0000627
2	pull	0.0000575
1	pulled	0.0001187
2	pulled	0.0000613
1	pulling	0.0000469
2	pulling	0.0000257
1	pullout	0.0000000
2	pullout	0.0000351
1	pulse	0.0000161
2	pulse	0.0000122
1	pump	0.0000502
2	pump	0.0000000
1	pumped	0.0000211
2	pumped	0.0000126
1	pumps	0.0000837
2	pumps	0.0000000
1	punish	0.0000025
2	punish	0.0000333
1	punished	0.0000000
2	punished	0.0000390
1	punishment	0.0000000
2	punishment	0.0001519
1	punitive	0.0000000
2	punitive	0.0000312
1	punjab	0.0000502
2	punjab	0.0000000
1	puppet	0.0000000
2	puppet	0.0000234
1	purchase	0.0002445
2	purchase	0.0000280
1	purchased	0.0001135
2	purchased	0.0000571
1	purchases	0.0001337
2	purchases	0.0000080
1	purchasing	0.0001228
2	purchasing	0.0000000
1	pure	0.0000384
2	pure	0.0000238
1	purple	0.0000058
2	purple	0.0000271
1	purpose	0.0000404
2	purpose	0.0001198
1	purposes	0.0000285
2	purposes	0.0000502
1	pursue	0.0000574
2	pursue	0.0000807
1	pursued	0.0000000
2	pursued	0.0000468
1	pursuing	0.0000000
2	pursuing	0.0000234
1	pursuit	0.0000261
2	pursuit	0.0000207
1	push	0.0001290
2	push	0.0001048
1	pushed	0.0002069
2	pushed	0.0000738
1	pushing	0.0001080
2	pushing	0.0000609
1	put	0.0005899
2	put	0.0008350
1	putnam	0.0000000
2	putnam	0.0000273
1	puts	0.0000497
2	puts	0.0000004
1	putting	0.0001312
2	putting	0.0000916
1	pw	0.0000000
2	pw	0.0000273
1	pyongyang	0.0000000
2	pyongyang	0.0000234
1	q	0.0000000
2	q	0.0000857
1	qatar	0.0000004
2	qatar	0.0000504
1	quake	0.0002623
2	quake	0.0000000
1	quaker	0.0000447
2	quaker	0.0000000
1	quakes	0.0000391
2	quakes	0.0000000
1	qualified	0.0000000
2	qualified	0.0000623
1	qualify	0.0000370
2	qualify	0.0000131
1	quality	0.0002891
2	quality	0.0000359
1	quantities	0.0000316
2	quantities	0.0000052
1	quantity	0.0000269
2	quantity	0.0000085
1	quarter	0.0007424
2	quarter	0.0000000
1	quarterly	0.0001172
2	quarterly	0.0000000
1	quarters	0.0000710
2	quarters	0.0000128
1	quartet	0.0000390
2	quartet	0.0000000
1	quayle	0.0000000
2	quayle	0.0002338
1	quayles	0.0000000
2	quayles	0.0000234
1	quebec	0.0000067
2	quebec	0.0000343
1	queen	0.0000081
2	queen	0.0000684
1	quell	0.0000000
2	quell	0.0000234
1	question	0.0000632
2	question	0.0004935
1	questionable	0.0000313
2	questionable	0.0000210
1	questioned	0.0000530
2	questioned	0.0001617
1	questioning	0.0000087
2	questioning	0.0000991
1	questions	0.0001247
2	questions	0.0004701
1	quick	0.0000808
2	quick	0.0000800
1	quickly	0.0002061
2	quickly	0.0001951
1	quiet	0.0000760
2	quiet	0.0000366
1	quietly	0.0000187
2	quietly	0.0000376
1	quinn	0.0000000
2	quinn	0.0000390
1	quints	0.0000000
2	quints	0.0000234
1	quit	0.0000296
2	quit	0.0001040
1	quite	0.0000911
2	quite	0.0001157
1	quo	0.0000000
2	quo	0.0000273
1	quota	0.0000446
2	quota	0.0000000
1	quotas	0.0000449
2	quotas	0.0000154
1	quoted	0.0004100
2	quoted	0.0003411
1	quotes	0.0000134
2	quotes	0.0000140
1	quoting	0.0000066
2	quoting	0.0000382
1	r	0.0001461
2	r	0.0001785
1	rabbi	0.0000000
2	rabbi	0.0000429
1	rabuka	0.0000000
2	rabuka	0.0000273
1	race	0.0000789
2	race	0.0004280
1	raced	0.0000273
2	raced	0.0000043
1	races	0.0000143
2	races	0.0000952
1	racial	0.0000000
2	racial	0.0001987
1	racially	0.0000000
2	racially	0.0000662
1	racing	0.0000781
2	racing	0.0000000
1	racism	0.0000000
2	racism	0.0000390
1	racist	0.0000000
2	racist	0.0000584
1	racketeering	0.0000001
2	racketeering	0.0000817
1	radakovich	0.0000000
2	radakovich	0.0000233
1	radar	0.0002233
2	radar	0.0000000
1	radiation	0.0001245
2	radiation	0.0000066
1	radical	0.0000079
2	radical	0.0001425
1	radicals	0.0000000
2	radicals	0.0000896
1	radio	0.0002354
2	radio	0.0006499
1	radioactive	0.0001045
2	radioactive	0.0000011
1	rafsanjani	0.0000000
2	rafsanjani	0.0000273
1	rafts	0.0000335
2	rafts	0.0000000
1	raid	0.0000556
2	raid	0.0000157
1	raided	0.0000226
2	raided	0.0000232
1	rail	0.0000898
2	rail	0.0000075
1	railroad	0.0001690
2	railroad	0.0000028
1	railroads	0.0000661
2	railroads	0.0000006
1	railway	0.0000500
2	railway	0.0000001
1	rain	0.0005861
2	rain	0.0000000
1	rainbow	0.0000000
2	rainbow	0.0000234
1	rainfall	0.0000781
2	rainfall	0.0000000
1	rains	0.0000670
2	rains	0.0000000
1	raisa	0.0000000
2	raisa	0.0000234
1	raise	0.0002153
2	raise	0.0002004
1	raised	0.0003515
2	raised	0.0002650
1	raises	0.0000733
2	raises	0.0000345
1	raising	0.0001930
2	raising	0.0001263
1	rajneesh	0.0000447
2	rajneesh	0.0000000
1	rakowski	0.0000000
2	rakowski	0.0000234
1	raleigh	0.0000296
2	raleigh	0.0000105
1	rallied	0.0000295
2	rallied	0.0000301
1	rallies	0.0000266
2	rallies	0.0000633
1	rally	0.0001640
2	rally	0.0003180
1	ralph	0.0000370
2	ralph	0.0000560
1	rambo	0.0000000
2	rambo	0.0000234
1	ramon	0.0000005
2	ramon	0.0000386
1	ramp	0.0000097
2	ramp	0.0000244
1	ramsey	0.0000056
2	ramsey	0.0000233
1	ramstein	0.0000558
2	ramstein	0.0000000
1	ran	0.0001970
2	ran	0.0001742
1	ranch	0.0000966
2	ranch	0.0000806
1	rancher	0.0000002
2	rancher	0.0000232
1	rancho	0.0000232
2	rancho	0.0000149
1	rand	0.0000335
2	rand	0.0000000
1	randolph	0.0000000
2	randolph	0.0000312
1	random	0.0000387
2	random	0.0000314
1	randomly	0.0000166
2	randomly	0.0000157
1	randy	0.0000150
2	randy	0.0000207
1	range	0.0003481
2	range	0.0000921
1	ranged	0.0001391
2	ranged	0.0000314
1	ranger	0.0000179
2	ranger	0.0000148
1	ranges	0.0000369
2	ranges	0.0000054
1	ranging	0.0000649
2	ranging	0.0000560
1	rangoon	0.0001228
2	rangoon	0.0000000
1	rank	0.0000257
2	rank	0.0000327
1	rankandfile	0.0000000
2	rankandfile	0.0000390
1	ranked	0.0001057
2	ranked	0.0000002
1	ranking	0.0000843
2	ranking	0.0000386
1	ranks	0.0000509
2	ranks	0.0000736
1	ransom	0.0000432
2	ransom	0.0000088
1	rape	0.0000076
2	rape	0.0001194
1	raped	0.0000000
2	raped	0.0000312
1	rapid	0.0000802
2	rapid	0.0000297
1	rapidly	0.0000626
2	rapidly	0.0000226
1	raping	0.0000000
2	raping	0.0000273
1	rappaport	0.0000614
2	rappaport	0.0000000
1	rare	0.0000855
2	rare	0.0000923
1	rarely	0.0000464
2	rarely	0.0000144
1	rash	0.0000290
2	rash	0.0000109
1	rate	0.0015238
2	rate	0.0000000
1	rated	0.0000782
2	rated	0.0000038
1	rates	0.0015232
2	rates	0.0000004
1	ratification	0.0000000
2	ratification	0.0000623
1	ratified	0.0000183
2	ratified	0.0000262
1	rating	0.0002728
2	rating	0.0000083
1	ratings	0.0002015
2	ratings	0.0000074
1	rational	0.0000109
2	rational	0.0000196
1	rationing	0.0000198
2	rationing	0.0000369
1	raton	0.0000318
2	raton	0.0000012
1	raul	0.0000000
2	raul	0.0000234
1	raw	0.0001060
2	raw	0.0000000
1	ray	0.0000108
2	ray	0.0001522
1	raymond	0.0000000
2	raymond	0.0000662
1	rays	0.0000000
2	rays	0.0000234
1	rca	0.0000000
2	rca	0.0000234
1	rd	0.0000480
2	rd	0.0000483
1	reach	0.0003247
2	reach	0.0001279
1	reached	0.0004401
2	reached	0.0003006
1	reaches	0.0000307
2	reaches	0.0000058
1	reaching	0.0000768
2	reaching	0.0000672
1	react	0.0000548
2	react	0.0000124
1	reacted	0.0000120
2	reacted	0.0000228
1	reaction	0.0000782
2	reaction	0.0001090
1	reactions	0.0000000
2	reactions	0.0000234
1	reactor	0.0000558
2	reactor	0.0000000
1	reactors	0.0000500
2	reactors	0.0000001
1	read	0.0000409
2	read	0.0002403
1	readily	0.0000141
2	readily	0.0000175
1	reading	0.0001441
2	reading	0.0001059
1	readings	0.0000391
2	readings	0.0000000
1	reads	0.0000216
2	reads	0.0000239
1	ready	0.0001048
2	ready	0.0003515
1	reaffirmed	0.0000141
2	reaffirmed	0.0000252
1	reagan	0.0000319
2	reagan	0.0014387
1	reagans	0.0000000
2	reagans	0.0002922
1	real	0.0005446
2	real	0.0003718
1	realistic	0.0000147
2	realistic	0.0000482
1	reality	0.0000220
2	reality	0.0000899
1	realize	0.0000745
2	realize	0.0000610
1	realized	0.0000043
2	realized	0.0000437
1	realizing	0.0000229
2	realizing	0.0000074
1	really	0.0002509
2	really	0.0004210
1	realtors	0.0000399
2	realtors	0.0000189
1	rear	0.0001172
2	rear	0.0000000
1	reason	0.0002303
2	reason	0.0002522
1	reasonable	0.0000738
2	reasonable	0.0000654
1	reasonably	0.0000011
2	reasonably	0.0000226
1	reasons	0.0000951
2	reasons	0.0001986
1	rebel	0.0000000
2	rebel	0.0002221
1	rebellion	0.0000000
2	rebellion	0.0000351
1	rebels	0.0000000
2	rebels	0.0006233
1	rebuffed	0.0000000
2	rebuffed	0.0000312
1	rebuild	0.0000484
2	rebuild	0.0000208
1	rebuilding	0.0000170
2	rebuilding	0.0000115
1	rebuilt	0.0000168
2	rebuilt	0.0000117
1	recall	0.0000240
2	recall	0.0000573
1	recalled	0.0000703
2	recalled	0.0000951
1	recalling	0.0000157
2	recalling	0.0000241
1	recalls	0.0000220
2	recalls	0.0000158
1	receipts	0.0000699
2	receipts	0.0000175
1	receive	0.0002118
2	receive	0.0002574
1	received	0.0004388
2	received	0.0006560
1	receives	0.0000143
2	receives	0.0000446
1	receiving	0.0001005
2	receiving	0.0001091
1	recent	0.0006374
2	recent	0.0006421
1	recently	0.0003648
2	recently	0.0003726
1	reception	0.0000017
2	reception	0.0000611
1	receptor	0.0000446
2	receptor	0.0000000
1	recess	0.0000000
2	recess	0.0000312
1	recession	0.0004075
2	recession	0.0000000
1	recipients	0.0000081
2	recipients	0.0000878
1	reckless	0.0000174
2	reckless	0.0000151
1	recognition	0.0000308
2	recognition	0.0000876
1	recognizance	0.0000034
2	recognizance	0.0000249
1	recognize	0.0000267
2	recognize	0.0001372
1	recognized	0.0000227
2	recognized	0.0001010
1	recommend	0.0000371
2	recommend	0.0000443
1	recommendation	0.0000493
2	recommendation	0.0000474
1	recommendations	0.0000117
2	recommendations	0.0001009
1	recommended	0.0001393
2	recommended	0.0000781
1	recommends	0.0000307
2	recommends	0.0000020
1	reconciliation	0.0000000
2	reconciliation	0.0000506
1	reconsider	0.0000094
2	reconsider	0.0000168
1	reconstruction	0.0000000
2	reconstruction	0.0000273
1	record	0.0010280
2	record	0.0002720
1	recorded	0.0002164
2	recorded	0.0000204
1	recorder	0.0000558
2	recorder	0.0000000
1	recording	0.0000481
2	recording	0.0000522
1	recordings	0.0000089
2	recordings	0.0000211
1	recordkeeping	0.0000391
2	recordkeeping	0.0000000
1	records	0.0002881
2	records	0.0002509
1	recover	0.0000500
2	recover	0.0000274
1	recovered	0.0002725
2	recovered	0.0000046
1	recovering	0.0000382
2	recovering	0.0000474
1	recovery	0.0001482
2	recovery	0.0000407
1	recreation	0.0000474
2	recreation	0.0000019
1	recruit	0.0000000
2	recruit	0.0000896
1	recruits	0.0000181
2	recruits	0.0000224
1	recycle	0.0000317
2	recycle	0.0000012
1	recycled	0.0000391
2	recycled	0.0000000
1	recycling	0.0001228
2	recycling	0.0000000
1	red	0.0002963
2	red	0.0003075
1	redman	0.0000000
2	redman	0.0000429
1	reduce	0.0003326
2	reduce	0.0001886
1	reduced	0.0002449
2	reduced	0.0000667
1	reducing	0.0001214
2	reducing	0.0001139
1	reduction	0.0001950
2	reduction	0.0001288
1	reductions	0.0000933
2	reductions	0.0000751
1	reed	0.0000063
2	reed	0.0001475
1	reef	0.0000391
2	reef	0.0000000
1	reelected	0.0000000
2	reelected	0.0000273
1	reelection	0.0000000
2	reelection	0.0001169
1	refer	0.0000000
2	refer	0.0000390
1	reference	0.0000075
2	reference	0.0000844
1	references	0.0000095
2	references	0.0000284
1	referendum	0.0000000
2	referendum	0.0001013
1	referred	0.0000115
2	referred	0.0001167
1	referring	0.0000255
2	referring	0.0002043
1	refined	0.0000475
2	refined	0.0000136
1	refinery	0.0000447
2	refinery	0.0000000
1	refining	0.0000447
2	refining	0.0000000
1	reflect	0.0000637
2	reflect	0.0000452
1	reflected	0.0001161
2	reflected	0.0000125
1	reflecting	0.0000932
2	reflecting	0.0000245
1	reflection	0.0000308
2	reflection	0.0000214
1	reflects	0.0000717
2	reflects	0.0000551
1	reform	0.0000000
2	reform	0.0004597
1	reformers	0.0000000
2	reformers	0.0000390
1	reformist	0.0000000
2	reformist	0.0000234
1	reforms	0.0000102
2	reforms	0.0002928
1	refrain	0.0000152
2	refrain	0.0000128
1	refrigerator	0.0000335
2	refrigerator	0.0000000
1	refuge	0.0000478
2	refuge	0.0000991
1	refugee	0.0000421
2	refugee	0.0000407
1	refugees	0.0000265
2	refugees	0.0001295
1	refunding	0.0000391
2	refunding	0.0000000
1	refusal	0.0000085
2	refusal	0.0000875
1	refuse	0.0000507
2	refuse	0.0000737
1	refused	0.0001467
2	refused	0.0006768
1	refuseniks	0.0000000
2	refuseniks	0.0000234
1	refuses	0.0000098
2	refuses	0.0000594
1	refusing	0.0000000
2	refusing	0.0000974
1	regain	0.0000089
2	regain	0.0000328
1	regained	0.0000462
2	regained	0.0000028
1	regan	0.0000000
2	regan	0.0000506
1	regard	0.0000147
2	regard	0.0000871
1	regarded	0.0000235
2	regarded	0.0000693
1	regarding	0.0000246
2	regarding	0.0000763
1	regardless	0.0000242
2	regardless	0.0000494
1	regime	0.0000000
2	regime	0.0001636
1	regimes	0.0000000
2	regimes	0.0000273
1	region	0.0002869
2	region	0.0002400
1	regional	0.0002662
2	regional	0.0001532
1	regions	0.0000737
2	regions	0.0000810
1	register	0.0000650
2	register	0.0000325
1	registered	0.0001902
2	registered	0.0000815
1	registration	0.0000023
2	registration	0.0000568
1	regret	0.0000000
2	regret	0.0000584
1	regular	0.0001815
2	regular	0.0000564
1	regularly	0.0000699
2	regularly	0.0000564
1	regulate	0.0000174
2	regulate	0.0000307
1	regulated	0.0000395
2	regulated	0.0000231
1	regulates	0.0000390
2	regulates	0.0000000
1	regulation	0.0000759
2	regulation	0.0000717
1	regulations	0.0002307
2	regulations	0.0000922
1	regulator	0.0000000
2	regulator	0.0000468
1	regulators	0.0000480
2	regulators	0.0001730
1	regulatory	0.0001171
2	regulatory	0.0000040
1	rehabilitation	0.0000353
2	rehabilitation	0.0000416
1	rehnquist	0.0000000
2	rehnquist	0.0000273
1	reich	0.0000048
2	reich	0.0000278
1	reid	0.0000107
2	reid	0.0000237
1	reign	0.0000000
2	reign	0.0000312
1	reimbursement	0.0000095
2	reimbursement	0.0000206
1	reimbursements	0.0000000
2	reimbursements	0.0000234
1	reinforced	0.0000251
2	reinforced	0.0000098
1	reinforcements	0.0000204
2	reinforcements	0.0000208
1	reinstated	0.0000000
2	reinstated	0.0000234
1	reiterate	0.0000042
2	reiterate	0.0000204
1	reiterated	0.0000000
2	reiterated	0.0000779
1	reject	0.0000000
2	reject	0.0000857
1	rejected	0.0001357
2	rejected	0.0003572
1	rejecting	0.0000000
2	rejecting	0.0000312
1	rejection	0.0000004
2	rejection	0.0000309
1	related	0.0001091
2	related	0.0001927
1	relating	0.0000131
2	relating	0.0000181
1	relation	0.0000272
2	relation	0.0000239
1	relations	0.0000147
2	relations	0.0005936
1	relationship	0.0000003
2	relationship	0.0002219
1	relationships	0.0000104
2	relationships	0.0000473
1	relative	0.0000730
2	relative	0.0000426
1	relatively	0.0001749
2	relatively	0.0000532
1	relatives	0.0001065
2	relatives	0.0001477
1	relax	0.0000175
2	relax	0.0000150
1	relaxed	0.0000135
2	relaxed	0.0000412
1	relay	0.0000335
2	relay	0.0000000
1	relayed	0.0000325
2	relayed	0.0000007
1	release	0.0002368
2	release	0.0005048
1	released	0.0006928
2	released	0.0005567
1	releases	0.0000428
2	releases	0.0000558
1	releasing	0.0000203
2	releasing	0.0000521
1	relevant	0.0000000
2	relevant	0.0000429
1	reliable	0.0000305
2	reliable	0.0000216
1	reliance	0.0000387
2	reliance	0.0000119
1	relied	0.0000092
2	relied	0.0000170
1	relief	0.0002832
2	relief	0.0000088
1	relieve	0.0000141
2	relieve	0.0000136
1	relieved	0.0000142
2	relieved	0.0000290
1	religion	0.0000000
2	religion	0.0000584
1	religious	0.0000000
2	religious	0.0002961
1	relinquish	0.0000000
2	relinquish	0.0000351
1	reluctance	0.0000163
2	reluctance	0.0000120
1	reluctant	0.0000485
2	reluctant	0.0000441
1	rely	0.0000623
2	rely	0.0000111
1	relying	0.0000222
2	relying	0.0000118
1	remain	0.0002505
2	remain	0.0003979
1	remainder	0.0000573
2	remainder	0.0000146
1	remained	0.0002629
2	remained	0.0001515
1	remaining	0.0001491
2	remaining	0.0001881
1	remains	0.0002085
2	remains	0.0003571
1	remark	0.0000053
2	remark	0.0000197
1	remarkable	0.0000257
2	remarkable	0.0000405
1	remarkably	0.0000088
2	remarkably	0.0000289
1	remarks	0.0000000
2	remarks	0.0002299
1	remember	0.0000322
2	remember	0.0000905
1	reminded	0.0000145
2	reminded	0.0000444
1	remote	0.0000967
2	remote	0.0000182
1	removal	0.0000131
2	removal	0.0000921
1	remove	0.0001329
2	remove	0.0001410
1	removed	0.0001219
2	removed	0.0002071
1	removing	0.0000721
2	removing	0.0000393
1	renew	0.0000001
2	renew	0.0000467
1	renewal	0.0000052
2	renewal	0.0000198
1	renewed	0.0000539
2	renewed	0.0000909
1	renounce	0.0000000
2	renounce	0.0000234
1	rent	0.0000206
2	rent	0.0000830
1	rental	0.0000448
2	rental	0.0000116
1	rented	0.0000252
2	rented	0.0000448
1	rents	0.0000206
2	rents	0.0000168
1	reopen	0.0000184
2	reopen	0.0000456
1	reopened	0.0000381
2	reopened	0.0000279
1	reopening	0.0000201
2	reopening	0.0000210
1	reorganization	0.0000443
2	reorganization	0.0000275
1	reorganize	0.0000239
2	reorganize	0.0000145
1	rep	0.0000000
2	rep	0.0008766
1	repair	0.0001384
2	repair	0.0000242
1	repairs	0.0000502
2	repairs	0.0000000
1	reparations	0.0000000
2	reparations	0.0000273
1	repatriation	0.0000000
2	repatriation	0.0000351
1	repay	0.0000098
2	repay	0.0000243
1	repayment	0.0000325
2	repayment	0.0000007
1	repeal	0.0000001
2	repeal	0.0000779
1	repeat	0.0000325
2	repeat	0.0000436
1	repeated	0.0000583
2	repeated	0.0001268
1	repeatedly	0.0000446
2	repeatedly	0.0001948
1	repeating	0.0000000
2	repeating	0.0000312
1	replace	0.0001357
2	replace	0.0001040
1	replaced	0.0001111
2	replaced	0.0000744
1	replacement	0.0001216
2	replacement	0.0000320
1	replacements	0.0000124
2	replacements	0.0000147
1	replacing	0.0000232
2	replacing	0.0000344
1	replied	0.0000313
2	replied	0.0001145
1	reply	0.0000004
2	reply	0.0000348
1	report	0.0023619
2	report	0.0006889
1	reported	0.0019922
2	reported	0.0007172
1	reportedly	0.0000806
2	reportedly	0.0001230
1	reporter	0.0000048
2	reporter	0.0001720
1	reporters	0.0000697
2	reporters	0.0009838
1	reporting	0.0001208
2	reporting	0.0000599
1	reports	0.0006799
2	reports	0.0004565
1	represent	0.0000858
2	represent	0.0000960
1	representation	0.0000000
2	representation	0.0000584
1	representative	0.0000562
2	representative	0.0001751
1	representatives	0.0000175
2	representatives	0.0002605
1	represented	0.0000349
2	represented	0.0001042
1	representing	0.0001087
2	representing	0.0001345
1	represents	0.0001625
2	represents	0.0000775
1	repression	0.0000000
2	repression	0.0000273
1	reprisals	0.0000059
2	reprisals	0.0000192
1	reps	0.0000000
2	reps	0.0000234
1	republic	0.0001086
2	republic	0.0003878
1	republican	0.0000000
2	republican	0.0011571
1	republicans	0.0000000
2	republicans	0.0003701
1	republicbank	0.0000601
2	republicbank	0.0000009
1	republics	0.0000000
2	republics	0.0003312
1	repurchase	0.0000248
2	repurchase	0.0000060
1	reputation	0.0000393
2	reputation	0.0000583
1	reputed	0.0000092
2	reputed	0.0000559
1	request	0.0000764
2	request	0.0004259
1	requested	0.0000459
2	requested	0.0001004
1	requesting	0.0000074
2	requesting	0.0000221
1	requests	0.0000543
2	requests	0.0001218
1	require	0.0001261
2	require	0.0002392
1	required	0.0003471
2	required	0.0002058
1	requirement	0.0000117
2	requirement	0.0000698
1	requirements	0.0002114
2	requirements	0.0000589
1	requires	0.0001420
2	requires	0.0001229
1	requiring	0.0000605
2	requiring	0.0000630
1	rescue	0.0002623
2	rescue	0.0000000
1	rescued	0.0001050
2	rescued	0.0000007
1	rescuers	0.0000391
2	rescuers	0.0000000
1	research	0.0008886
2	research	0.0000265
1	researcher	0.0000587
2	researcher	0.0000097
1	researchers	0.0004404
2	researchers	0.0000004
1	resent	0.0000000
2	resent	0.0000273
1	reservation	0.0001171
2	reservation	0.0000001
1	reservations	0.0000248
2	reservations	0.0000606
1	reserve	0.0004713
2	reserve	0.0000256
1	reserved	0.0000168
2	reserved	0.0000117
1	reserves	0.0001163
2	reserves	0.0000162
1	reservoir	0.0000629
2	reservoir	0.0000067
1	resettlement	0.0000122
2	resettlement	0.0000305
1	residence	0.0000236
2	residence	0.0000809
1	residency	0.0000000
2	residency	0.0000234
1	resident	0.0000551
2	resident	0.0000706
1	residential	0.0000814
2	residential	0.0000016
1	residents	0.0005379
2	residents	0.0002128
1	resign	0.0000016
2	resign	0.0001781
1	resignation	0.0000571
2	resignation	0.0001432
1	resignations	0.0000105
2	resignations	0.0000277
1	resigned	0.0000335
2	resigned	0.0001325
1	resigning	0.0000231
2	resigning	0.0000384
1	resist	0.0000232
2	resist	0.0000266
1	resistance	0.0000502
2	resistance	0.0001598
1	resisted	0.0000073
2	resisted	0.0000494
1	resisting	0.0000101
2	resisting	0.0000163
1	resolution	0.0000129
2	resolution	0.0003339
1	resolutions	0.0000000
2	resolutions	0.0000740
1	resolve	0.0000153
2	resolve	0.0001568
1	resolved	0.0000331
2	resolved	0.0000626
1	resolving	0.0000140
2	resolving	0.0000292
1	resort	0.0000918
2	resort	0.0000645
1	resource	0.0000448
2	resource	0.0000077
1	resources	0.0002233
2	resources	0.0001519
1	respect	0.0000000
2	respect	0.0001325
1	respectable	0.0000076
2	respectable	0.0000181
1	respected	0.0000000
2	respected	0.0000506
1	respective	0.0000162
2	respective	0.0000199
1	respectively	0.0000520
2	respectively	0.0000027
1	respects	0.0000095
2	respects	0.0000246
1	respond	0.0000639
2	respond	0.0001346
1	responded	0.0000483
2	responded	0.0001844
1	respondents	0.0000283
2	respondents	0.0000309
1	responding	0.0000569
2	responding	0.0000538
1	response	0.0001301
2	response	0.0002793
1	responses	0.0000168
2	responses	0.0000156
1	responsibilities	0.0000293
2	responsibilities	0.0000146
1	responsibility	0.0000215
2	responsibility	0.0002850
1	responsible	0.0001312
2	responsible	0.0002045
1	responsive	0.0000364
2	responsive	0.0000057
1	rest	0.0003529
2	rest	0.0001705
1	restaurant	0.0001189
2	restaurant	0.0000728
1	restaurants	0.0001382
2	restaurants	0.0000165
1	resting	0.0000389
2	resting	0.0000001
1	restitution	0.0000000
2	restitution	0.0000273
1	restoration	0.0000075
2	restoration	0.0000805
1	restore	0.0000459
2	restore	0.0000810
1	restored	0.0000000
2	restored	0.0000818
1	restoring	0.0000323
2	restoring	0.0000437
1	restrain	0.0000327
2	restrain	0.0000045
1	restraining	0.0000238
2	restraining	0.0000146
1	restraint	0.0000186
2	restraint	0.0000650
1	restrict	0.0000025
2	restrict	0.0000294
1	restricted	0.0000031
2	restricted	0.0000680
1	restricting	0.0000011
2	restricting	0.0000382
1	restriction	0.0000011
2	restriction	0.0000226
1	restrictions	0.0000970
2	restrictions	0.0001700
1	restructure	0.0000227
2	restructure	0.0000270
1	restructuring	0.0001339
2	restructuring	0.0000896
1	result	0.0003417
2	result	0.0002913
1	resulted	0.0001195
2	resulted	0.0000919
1	resulting	0.0000616
2	resulting	0.0000466
1	results	0.0004269
2	results	0.0002163
1	resume	0.0000830
2	resume	0.0000940
1	resumed	0.0000399
2	resumed	0.0000501
1	resuming	0.0000064
2	resuming	0.0000189
1	retail	0.0002121
2	retail	0.0000000
1	retailer	0.0000670
2	retailer	0.0000000
1	retailers	0.0001228
2	retailers	0.0000000
1	retailing	0.0000502
2	retailing	0.0000000
1	retain	0.0000191
2	retain	0.0000529
1	retained	0.0000007
2	retained	0.0000424
1	retaliation	0.0000218
2	retaliation	0.0000316
1	retarded	0.0000000
2	retarded	0.0000390
1	retire	0.0000420
2	retire	0.0000564
1	retired	0.0000288
2	retired	0.0001708
1	retirement	0.0000394
2	retirement	0.0000660
1	retiring	0.0000000
2	retiring	0.0000584
1	retreat	0.0000090
2	retreat	0.0000560
1	retreated	0.0000546
2	retreated	0.0000047
1	retribution	0.0000386
2	retribution	0.0000003
1	return	0.0003732
2	return	0.0005810
1	returned	0.0002597
2	returned	0.0003525
1	returning	0.0000794
2	returning	0.0001939
1	returns	0.0001963
2	returns	0.0001084
1	reunification	0.0000058
2	reunification	0.0000466
1	reunion	0.0000108
2	reunion	0.0000197
1	rev	0.0000000
2	rev	0.0002143
1	revco	0.0000335
2	revco	0.0000000
1	reveal	0.0000284
2	reveal	0.0000230
1	revealed	0.0000623
2	revealed	0.0000812
1	revenge	0.0000101
2	revenge	0.0000553
1	revenue	0.0003614
2	revenue	0.0000010
1	revenues	0.0001384
2	revenues	0.0000242
1	reverse	0.0000420
2	reverse	0.0000525
1	reversed	0.0000349
2	reversed	0.0000496
1	reversing	0.0000121
2	reversing	0.0000188
1	review	0.0001106
2	review	0.0003864
1	reviewed	0.0000271
2	reviewed	0.0000473
1	reviewing	0.0000269
2	reviewing	0.0000435
1	reviews	0.0000000
2	reviews	0.0000234
1	revised	0.0001029
2	revised	0.0000100
1	revision	0.0000824
2	revision	0.0000010
1	revival	0.0000388
2	revival	0.0000158
1	revive	0.0000201
2	revive	0.0000288
1	revived	0.0000223
2	revived	0.0000078
1	revoking	0.0000002
2	revoking	0.0000232
1	revolt	0.0000001
2	revolt	0.0000350
1	revolution	0.0000008
2	revolution	0.0002176
1	revolutionary	0.0000000
2	revolutionary	0.0000818
1	revolver	0.0000334
2	revolver	0.0000001
1	reward	0.0000022
2	reward	0.0000413
1	rewrite	0.0000000
2	rewrite	0.0000273
1	reynolds	0.0002344
2	reynolds	0.0000000
1	rhetoric	0.0000000
2	rhetoric	0.0000701
1	rhode	0.0000176
2	rhode	0.0000150
1	ri	0.0000348
2	ri	0.0000069
1	rica	0.0000126
2	rica	0.0000535
1	rican	0.0000037
2	rican	0.0000208
1	ricardo	0.0000026
2	ricardo	0.0000255
1	rice	0.0001531
2	rice	0.0000217
1	rich	0.0000726
2	rich	0.0000662
1	richard	0.0002631
2	richard	0.0004592
1	richards	0.0000000
2	richards	0.0000468
1	richardson	0.0000083
2	richardson	0.0000293
1	richest	0.0000376
2	richest	0.0000127
1	richfield	0.0000558
2	richfield	0.0000000
1	richman	0.0000294
2	richman	0.0000185
1	richmond	0.0000450
2	richmond	0.0000192
1	richter	0.0001786
2	richter	0.0000000
1	rick	0.0000651
2	rick	0.0000052
1	ricky	0.0000000
2	ricky	0.0000351
1	rico	0.0000213
2	rico	0.0000708
1	rid	0.0000398
2	rid	0.0000112
1	ride	0.0000584
2	ride	0.0000060
1	ridge	0.0000837
2	ridge	0.0000000
1	ridiculous	0.0000000
2	ridiculous	0.0000351
1	riding	0.0000385
2	riding	0.0000394
1	ridley	0.0000063
2	ridley	0.0000345
1	riegle	0.0000000
2	riegle	0.0000623
1	rifle	0.0000799
2	rifle	0.0000689
1	rifles	0.0000083
2	rifles	0.0000488
1	rig	0.0000804
2	rig	0.0000062
1	riggs	0.0000381
2	riggs	0.0000007
1	right	0.0003155
2	right	0.0008823
1	rights	0.0000032
2	rights	0.0012796
1	rightwing	0.0000000
2	rightwing	0.0001247
1	rigs	0.0000391
2	rigs	0.0000000
1	rill	0.0000000
2	rill	0.0000312
1	rinfret	0.0000000
2	rinfret	0.0000234
1	ring	0.0000983
2	ring	0.0000833
1	rings	0.0000445
2	rings	0.0000001
1	rio	0.0001116
2	rio	0.0000000
1	rios	0.0000000
2	rios	0.0000312
1	riot	0.0000079
2	riot	0.0001036
1	rioters	0.0000501
2	rioters	0.0000001
1	rioting	0.0000054
2	rioting	0.0000664
1	riots	0.0000000
2	riots	0.0000896
1	ripped	0.0000349
2	ripped	0.0000068
1	rise	0.0005335
2	rise	0.0000172
1	risen	0.0001118
2	risen	0.0000116
1	rises	0.0000502
2	rises	0.0000000
1	rising	0.0004052
2	rising	0.0000405
1	risk	0.0002446
2	risk	0.0001176
1	risks	0.0000881
2	risks	0.0000320
1	risky	0.0000357
2	risky	0.0000141
1	rison	0.0000000
2	rison	0.0000273
1	ritalin	0.0000000
2	ritalin	0.0000351
1	ritual	0.0000000
2	ritual	0.0000351
1	rival	0.0000775
2	rival	0.0001290
1	rivals	0.0000193
2	rivals	0.0000762
1	river	0.0005820
2	river	0.0000379
1	rivers	0.0001288
2	rivers	0.0000075
1	riverside	0.0000316
2	riverside	0.0000052
1	rjr	0.0001172
2	rjr	0.0000000
1	rkan	0.0000000
2	rkan	0.0000390
1	rn	0.0000335
2	rn	0.0000000
1	rnc	0.0000000
2	rnc	0.0000351
1	rnh	0.0000000
2	rnh	0.0000429
1	rny	0.0000000
2	rny	0.0000273
1	road	0.0002555
2	road	0.0001840
1	roads	0.0001809
2	roads	0.0000257
1	robb	0.0000000
2	robb	0.0000974
1	robbed	0.0000134
2	robbed	0.0000374
1	robber	0.0000000
2	robber	0.0000234
1	robbery	0.0000710
2	robbery	0.0000517
1	robby	0.0000502
2	robby	0.0000000
1	roberson	0.0000447
2	roberson	0.0000000
1	robert	0.0004915
2	robert	0.0005491
1	roberto	0.0000000
2	roberto	0.0000273
1	roberts	0.0002610
2	roberts	0.0000749
1	robertson	0.0000000
2	robertson	0.0001519
1	robertsons	0.0000000
2	robertsons	0.0000390
1	robin	0.0000010
2	robin	0.0000421
1	robinson	0.0000001
2	robinson	0.0001246
1	rocard	0.0000305
2	rocard	0.0000177
1	rochester	0.0000391
2	rochester	0.0000000
1	rock	0.0002317
2	rock	0.0000642
1	rocked	0.0000136
2	rocked	0.0000295
1	rocket	0.0002121
2	rocket	0.0000000
1	rockets	0.0000542
2	rockets	0.0000323
1	rockies	0.0000502
2	rockies	0.0000000
1	rocks	0.0001553
2	rocks	0.0000007
1	rockwell	0.0000229
2	rockwell	0.0000152
1	rocky	0.0001001
2	rocky	0.0000197
1	rode	0.0000061
2	rode	0.0000347
1	rodriguez	0.0000307
2	rodriguez	0.0000331
1	roe	0.0000000
2	roe	0.0000234
1	roger	0.0000603
2	roger	0.0000631
1	rogers	0.0000313
2	rogers	0.0000288
1	roh	0.0000000
2	roh	0.0001286
1	role	0.0000436
2	role	0.0004838
1	roles	0.0000068
2	roles	0.0000381
1	roll	0.0000812
2	roll	0.0000329
1	rolled	0.0000748
2	rolled	0.0000179
1	rolling	0.0000343
2	rolling	0.0000306
1	roman	0.0000006
2	roman	0.0002996
1	romance	0.0000197
2	romance	0.0000096
1	romania	0.0000000
2	romania	0.0000584
1	romanian	0.0000000
2	romanian	0.0000428
1	romanias	0.0000000
2	romanias	0.0000234
1	romantic	0.0000158
2	romantic	0.0000162
1	rome	0.0000180
2	rome	0.0000965
1	romer	0.0000447
2	romer	0.0000000
1	ron	0.0000223
2	ron	0.0000818
1	ronald	0.0000418
2	ronald	0.0001306
1	roof	0.0000865
2	roof	0.0000097
1	roofs	0.0000447
2	roofs	0.0000000
1	room	0.0002827
2	room	0.0002195
1	rooms	0.0000449
2	rooms	0.0000388
1	roosevelt	0.0000075
2	roosevelt	0.0000649
1	root	0.0000000
2	root	0.0000545
1	roots	0.0000126
2	roots	0.0000185
1	rosa	0.0000361
2	rosa	0.0000020
1	rose	0.0017962
2	rose	0.0000553
1	roses	0.0000620
2	roses	0.0000113
1	ross	0.0000441
2	ross	0.0000004
1	rostenkowski	0.0000000
2	rostenkowski	0.0000545
1	rotation	0.0001005
2	rotation	0.0000000
1	roth	0.0000262
2	roth	0.0000051
1	rouge	0.0000060
2	rouge	0.0000620
1	rough	0.0000676
2	rough	0.0000190
1	roughly	0.0001202
2	roughly	0.0000174
1	round	0.0000872
2	round	0.0002586
1	rounds	0.0000295
2	rounds	0.0000496
1	roundtable	0.0000000
2	roundtable	0.0000234
1	route	0.0002296
2	route	0.0000228
1	routes	0.0000893
2	routes	0.0000000
1	routine	0.0001169
2	routine	0.0000002
1	routinely	0.0000163
2	routinely	0.0000392
1	rover	0.0000335
2	rover	0.0000000
1	row	0.0000781
2	row	0.0000429
1	rowan	0.0000000
2	rowan	0.0000623
1	roy	0.0000724
2	roy	0.0000585
1	royal	0.0000811
2	royal	0.0001187
1	royalties	0.0000173
2	royalties	0.0000230
1	rsqb	0.0001395
2	rsqb	0.0000000
1	rubber	0.0000435
2	rubber	0.0000125
1	rubbish	0.0000114
2	rubbish	0.0000154
1	rubin	0.0000000
2	rubin	0.0000312
1	ruby	0.0000157
2	ruby	0.0000553
1	rude	0.0000000
2	rude	0.0000234
1	rudman	0.0000000
2	rudman	0.0000468
1	ruffin	0.0000000
2	ruffin	0.0000390
1	ruined	0.0000146
2	ruined	0.0000132
1	ruins	0.0000558
2	ruins	0.0000000
1	rule	0.0000280
2	rule	0.0004869
1	ruled	0.0000428
2	ruled	0.0003714
1	ruler	0.0000000
2	ruler	0.0000312
1	rulers	0.0000000
2	rulers	0.0000350
1	rules	0.0001163
2	rules	0.0002227
1	ruling	0.0000000
2	ruling	0.0005844
1	rulings	0.0000000
2	rulings	0.0000312
1	rumored	0.0000228
2	rumored	0.0000191
1	rumors	0.0001447
2	rumors	0.0000314
1	run	0.0003158
2	run	0.0004185
1	runaway	0.0000293
2	runaway	0.0000068
1	rundown	0.0000249
2	rundown	0.0000099
1	runners	0.0000000
2	runners	0.0000273
1	running	0.0001304
2	running	0.0005284
1	runoff	0.0000064
2	runoff	0.0000891
1	runs	0.0001470
2	runs	0.0000766
1	runway	0.0000949
2	runway	0.0000000
1	rural	0.0002854
2	rural	0.0001358
1	rush	0.0000947
2	rush	0.0000119
1	rushed	0.0000338
2	rushed	0.0000388
1	rushing	0.0000240
2	rushing	0.0000105
1	russell	0.0000439
2	russell	0.0000823
1	russells	0.0000000
2	russells	0.0000234
1	russia	0.0000111
2	russia	0.0000234
1	russian	0.0000000
2	russian	0.0001753
1	russians	0.0000000
2	russians	0.0000234
1	rust	0.0000000
2	rust	0.0000545
1	rutah	0.0000000
2	rutah	0.0000273
1	ruth	0.0000000
2	ruth	0.0000545
1	ryan	0.0000163
2	ryan	0.0000665
1	ryzhkov	0.0000000
2	ryzhkov	0.0000623
1	ryzhkovs	0.0000000
2	ryzhkovs	0.0000273
1	sabotage	0.0000258
2	sabotage	0.0000287
1	sachs	0.0000329
2	sachs	0.0000160
1	sacramento	0.0000261
2	sacramento	0.0000168
1	sacrifice	0.0000111
2	sacrifice	0.0000390
1	sad	0.0000118
2	sad	0.0000385
1	sadat	0.0000000
2	sadat	0.0000468
1	saddam	0.0000137
2	saddam	0.0003138
1	saddened	0.0000002
2	saddened	0.0000233
1	safe	0.0003027
2	safe	0.0000692
1	safeguard	0.0000206
2	safeguard	0.0000090
1	safely	0.0001061
2	safely	0.0000000
1	safer	0.0000202
2	safer	0.0000092
1	safety	0.0007552
2	safety	0.0000884
1	saga	0.0000355
2	saga	0.0000103
1	sahara	0.0000056
2	sahara	0.0000273
1	sailed	0.0000002
2	sailed	0.0000349
1	sailors	0.0001353
2	sailors	0.0000108
1	saint	0.0000096
2	saint	0.0000440
1	saints	0.0000000
2	saints	0.0000234
1	saito	0.0000000
2	saito	0.0000312
1	sajudis	0.0000000
2	sajudis	0.0000662
1	sake	0.0000148
2	sake	0.0000130
1	sakharov	0.0000000
2	sakharov	0.0000935
1	salaries	0.0000995
2	salaries	0.0000319
1	salary	0.0001409
2	salary	0.0000497
1	sale	0.0005727
2	sale	0.0000678
1	sales	0.0017959
2	sales	0.0000594
1	salinas	0.0000000
2	salinas	0.0000818
1	sally	0.0000151
2	sally	0.0000128
1	salomon	0.0000315
2	salomon	0.0000053
1	salt	0.0001315
2	salt	0.0000017
1	salvador	0.0000014
2	salvador	0.0000964
1	salvadoran	0.0000005
2	salvadoran	0.0000425
1	salvage	0.0000837
2	salvage	0.0000000
1	salvation	0.0000067
2	salvation	0.0000421
1	sam	0.0000212
2	sam	0.0001138
1	sample	0.0000474
2	sample	0.0000410
1	samples	0.0000890
2	samples	0.0000002
1	samuel	0.0000564
2	samuel	0.0000619
1	san	0.0009306
2	san	0.0002310
1	sanctions	0.0000000
2	sanctions	0.0003623
1	sanctuary	0.0000351
2	sanctuary	0.0000105
1	sand	0.0001451
2	sand	0.0000000
1	sandinista	0.0000000
2	sandinista	0.0001870
1	sandinistas	0.0000000
2	sandinistas	0.0001792
1	sandra	0.0000217
2	sandra	0.0000199
1	sandy	0.0000084
2	sandy	0.0000175
1	sane	0.0000000
2	sane	0.0000234
1	sang	0.0000327
2	sang	0.0000746
1	sank	0.0001228
2	sank	0.0000000
1	santa	0.0002863
2	santa	0.0000222
1	santiago	0.0000380
2	santiago	0.0000163
1	sao	0.0000106
2	sao	0.0000238
1	sara	0.0000000
2	sara	0.0000350
1	sassan	0.0000335
2	sassan	0.0000000
1	sasser	0.0000000
2	sasser	0.0000779
1	sat	0.0000280
2	sat	0.0000935
1	satellite	0.0002233
2	satellite	0.0000000
1	satellites	0.0000337
2	satellites	0.0000193
1	satisfaction	0.0000093
2	satisfaction	0.0000286
1	satisfactory	0.0000217
2	satisfactory	0.0000121
1	satisfied	0.0000512
2	satisfied	0.0000539
1	satisfy	0.0000298
2	satisfy	0.0000260
1	saturday	0.0006097
2	saturday	0.0007042
1	saturdays	0.0000367
2	saturdays	0.0000562
1	sauce	0.0000173
2	sauce	0.0000152
1	saudi	0.0001267
2	saudi	0.0005037
1	saudis	0.0000003
2	saudis	0.0000427
1	saunders	0.0000680
2	saunders	0.0000187
1	savannah	0.0000781
2	savannah	0.0000000
1	save	0.0001728
2	save	0.0001131
1	saved	0.0000710
2	saved	0.0000011
1	saving	0.0000066
2	saving	0.0000266
1	savings	0.0003849
2	savings	0.0001560
1	saw	0.0003571
2	saw	0.0002299
1	say	0.0010944
2	say	0.0017763
1	saying	0.0002964
2	saying	0.0013126
1	says	0.0008352
2	says	0.0012910
1	sc	0.0000306
2	sc	0.0000215
1	scale	0.0002418
2	scale	0.0000143
1	scalia	0.0000000
2	scalia	0.0000351
1	scalpers	0.0000002
2	scalpers	0.0000232
1	scandal	0.0000000
2	scandal	0.0001325
1	scandals	0.0000000
2	scandals	0.0000273
1	scarce	0.0000471
2	scarce	0.0000022
1	scare	0.0000502
2	scare	0.0000117
1	scared	0.0000483
2	scared	0.0000287
1	scary	0.0000305
2	scary	0.0000021
1	scattered	0.0001590
2	scattered	0.0000176
1	scenario	0.0000378
2	scenario	0.0000126
1	scene	0.0001780
2	scene	0.0000939
1	scenes	0.0000232
2	scenes	0.0000422
1	schedule	0.0001144
2	schedule	0.0001188
1	scheduled	0.0003692
2	scheduled	0.0005877
1	schedules	0.0000428
2	schedules	0.0000052
1	scheme	0.0000012
2	scheme	0.0001200
1	scherer	0.0000000
2	scherer	0.0000273
1	scheringplough	0.0000335
2	scheringplough	0.0000000
1	schlesinger	0.0000332
2	schlesinger	0.0000002
1	schneidman	0.0000614
2	schneidman	0.0000000
1	scholar	0.0000000
2	scholar	0.0000429
1	scholars	0.0000096
2	scholars	0.0000205
1	school	0.0003421
2	school	0.0011326
1	schools	0.0000713
2	schools	0.0004100
1	schroeder	0.0000000
2	schroeder	0.0000234
1	schulz	0.0000391
2	schulz	0.0000000
1	schwarzkopf	0.0000000
2	schwarzkopf	0.0000701
1	science	0.0001128
2	science	0.0000966
1	sciences	0.0000428
2	sciences	0.0000246
1	scientific	0.0001408
2	scientific	0.0000420
1	scientist	0.0000851
2	scientist	0.0000341
1	scientists	0.0003670
2	scientists	0.0000321
1	scope	0.0000277
2	scope	0.0000274
1	score	0.0000334
2	score	0.0000273
1	scored	0.0000074
2	scored	0.0000260
1	scores	0.0000641
2	scores	0.0000565
1	scotland	0.0000528
2	scotland	0.0000216
1	scott	0.0000460
2	scott	0.0000458
1	scottish	0.0000132
2	scottish	0.0000142
1	scrap	0.0000377
2	scrap	0.0000166
1	scratch	0.0000072
2	scratch	0.0000183
1	screaming	0.0000372
2	screaming	0.0000247
1	screen	0.0000552
2	screen	0.0000433
1	screening	0.0000110
2	screening	0.0000352
1	screenplay	0.0000000
2	screenplay	0.0000273
1	scripps	0.0000391
2	scripps	0.0000000
1	script	0.0000004
2	script	0.0000348
1	scrutiny	0.0000350
2	scrutiny	0.0000146
1	sd	0.0000441
2	sd	0.0000082
1	sdi	0.0000000
2	sdi	0.0000273
1	sea	0.0003180
2	sea	0.0000663
1	seal	0.0000158
2	seal	0.0000123
1	sealed	0.0000167
2	sealed	0.0000624
1	search	0.0001218
2	search	0.0001721
1	searched	0.0000701
2	searched	0.0000407
1	searches	0.0000008
2	searches	0.0000384
1	searching	0.0000645
2	searching	0.0000368
1	sears	0.0000383
2	sears	0.0000083
1	seas	0.0000335
2	seas	0.0000000
1	season	0.0004785
2	season	0.0000439
1	seasonal	0.0000335
2	seasonal	0.0000000
1	seasonally	0.0000558
2	seasonally	0.0000000
1	seasons	0.0000569
2	seasons	0.0000070
1	seat	0.0000687
2	seat	0.0002364
1	seats	0.0000258
2	seats	0.0001690
1	seattle	0.0000812
2	seattle	0.0000290
1	sec	0.0000001
2	sec	0.0001051
1	secede	0.0000000
2	secede	0.0000234
1	secession	0.0000048
2	secession	0.0000317
1	second	0.0008752
2	second	0.0005735
1	secondary	0.0000410
2	secondary	0.0000103
1	seconddegree	0.0000033
2	seconddegree	0.0000289
1	secondlargest	0.0000614
2	secondlargest	0.0000000
1	secondquarter	0.0000781
2	secondquarter	0.0000000
1	seconds	0.0000956
2	seconds	0.0000112
1	secord	0.0000000
2	secord	0.0000234
1	secrecy	0.0000337
2	secrecy	0.0000427
1	secret	0.0000281
2	secret	0.0004713
1	secretariat	0.0000000
2	secretariat	0.0000234
1	secretaries	0.0000000
2	secretaries	0.0000390
1	secretary	0.0001064
2	secretary	0.0010867
1	secretarygeneral	0.0000000
2	secretarygeneral	0.0000935
1	secretly	0.0000000
2	secretly	0.0000545
1	secrets	0.0000025
2	secrets	0.0000606
1	section	0.0001285
2	section	0.0001129
1	sections	0.0000574
2	sections	0.0000379
1	sector	0.0002334
2	sector	0.0000202
1	sectors	0.0000282
2	sectors	0.0000349
1	secular	0.0000001
2	secular	0.0000272
1	secure	0.0000376
2	secure	0.0000633
1	securing	0.0000152
2	securing	0.0000166
1	securitate	0.0000000
2	securitate	0.0000468
1	securities	0.0007778
2	securities	0.0000104
1	security	0.0002095
2	security	0.0013888
1	sediment	0.0000335
2	sediment	0.0000000
1	sedlmayr	0.0000000
2	sedlmayr	0.0000312
1	see	0.0006257
2	see	0.0008178
1	seeing	0.0001049
2	seeing	0.0001255
1	seek	0.0000419
2	seek	0.0003993
1	seeking	0.0001247
2	seeking	0.0004700
1	seeks	0.0000000
2	seeks	0.0000701
1	seen	0.0004659
2	seen	0.0003410
1	sees	0.0000001
2	sees	0.0000623
1	segment	0.0000494
2	segment	0.0000162
1	segments	0.0000335
2	segments	0.0000000
1	segregated	0.0000000
2	segregated	0.0000429
1	segregation	0.0000000
2	segregation	0.0000506
1	seiders	0.0000276
2	seiders	0.0000080
1	seidman	0.0000391
2	seidman	0.0000000
1	seidon	0.0000447
2	seidon	0.0000000
1	sein	0.0000391
2	sein	0.0000000
1	seismic	0.0000391
2	seismic	0.0000000
1	seismographs	0.0000447
2	seismographs	0.0000000
1	seize	0.0000052
2	seize	0.0000237
1	seized	0.0000351
2	seized	0.0001820
1	seizure	0.0000284
2	seizure	0.0000230
1	seldom	0.0000118
2	seldom	0.0000151
1	select	0.0000270
2	select	0.0000357
1	selected	0.0001399
2	selected	0.0000815
1	selection	0.0000299
2	selection	0.0000610
1	selfdefense	0.0000000
2	selfdefense	0.0000390
1	selfdetermination	0.0000007
2	selfdetermination	0.0000268
1	sell	0.0007354
2	sell	0.0000204
1	sellers	0.0000502
2	sellers	0.0000000
1	selling	0.0005037
2	selling	0.0000458
1	selloff	0.0000335
2	selloff	0.0000000
1	sells	0.0000643
2	sells	0.0000057
1	semiconductor	0.0000726
2	semiconductor	0.0000000
1	semiconductors	0.0000253
2	semiconductors	0.0000057
1	sen	0.0000213
2	sen	0.0009981
1	senate	0.0000000
2	senate	0.0014025
1	senator	0.0000000
2	senator	0.0002221
1	senators	0.0000000
2	senators	0.0003156
1	send	0.0000675
2	send	0.0002567
1	sending	0.0000559
2	sending	0.0001363
1	sends	0.0000005
2	sends	0.0000464
1	senior	0.0003909
2	senior	0.0004089
1	seniority	0.0000000
2	seniority	0.0000234
1	sens	0.0000000
2	sens	0.0000662
1	sense	0.0001075
2	sense	0.0001899
1	sensible	0.0000196
2	sensible	0.0000253
1	sensitive	0.0000111
2	sensitive	0.0000974
1	sensitivity	0.0000061
2	sensitivity	0.0000269
1	sent	0.0003381
2	sent	0.0005744
1	sentence	0.0000000
2	sentence	0.0004052
1	sentenced	0.0000000
2	sentenced	0.0003818
1	sentences	0.0000015
2	sentences	0.0000769
1	sentencing	0.0000000
2	sentencing	0.0001091
1	sentiment	0.0000406
2	sentiment	0.0000340
1	seoul	0.0000182
2	seoul	0.0000964
1	separate	0.0001695
2	separate	0.0002674
1	separated	0.0000366
2	separated	0.0000290
1	separately	0.0000384
2	separately	0.0000472
1	separation	0.0000139
2	separation	0.0000215
1	separatist	0.0000126
2	separatist	0.0000263
1	separatists	0.0000335
2	separatists	0.0000000
1	sept	0.0003073
2	sept	0.0002647
1	september	0.0004788
2	september	0.0002229
1	sequence	0.0000379
2	sequence	0.0000008
1	serbia	0.0000056
2	serbia	0.0000234
1	serbian	0.0000000
2	serbian	0.0000234
1	sergei	0.0000000
2	sergei	0.0000312
1	series	0.0003366
2	series	0.0002442
1	serious	0.0004109
2	serious	0.0002703
1	seriously	0.0000984
2	seriously	0.0001339
1	servants	0.0000390
2	servants	0.0000000
1	serve	0.0001410
2	serve	0.0002211
1	served	0.0001106
2	served	0.0002851
1	serves	0.0000388
2	serves	0.0000391
1	service	0.0015698
2	service	0.0005367
1	servicemen	0.0000149
2	servicemen	0.0000870
1	services	0.0006555
2	services	0.0004190
1	serving	0.0000416
2	serving	0.0001619
1	session	0.0003788
2	session	0.0004096
1	sessions	0.0000654
2	sessions	0.0000946
1	setback	0.0000418
2	setback	0.0000137
1	setbacks	0.0000389
2	setbacks	0.0000118
1	seton	0.0000218
2	seton	0.0000081
1	sets	0.0000537
2	sets	0.0001105
1	setting	0.0000894
2	setting	0.0000896
1	settle	0.0000279
2	settle	0.0000896
1	settled	0.0002474
2	settled	0.0000728
1	settlement	0.0000615
2	settlement	0.0003467
1	settlements	0.0000176
2	settlements	0.0001046
1	settlers	0.0000159
2	settlers	0.0000318
1	settling	0.0000416
2	settling	0.0000216
1	seven	0.0005754
2	seven	0.0004321
1	sevenday	0.0000126
2	sevenday	0.0000146
1	seventh	0.0000583
2	seventh	0.0000138
1	sevenyear	0.0000138
2	sevenyear	0.0000176
1	severe	0.0001997
2	severe	0.0000827
1	severed	0.0000000
2	severed	0.0000312
1	severely	0.0000570
2	severely	0.0000304
1	severity	0.0000420
2	severity	0.0000135
1	sewage	0.0000837
2	sewage	0.0000000
1	sex	0.0000260
2	sex	0.0001143
1	sexual	0.0000282
2	sexual	0.0001323
1	sexuality	0.0000000
2	sexuality	0.0000429
1	sexually	0.0000115
2	sexually	0.0000271
1	sgt	0.0002071
2	sgt	0.0000191
1	shadow	0.0000253
2	shadow	0.0000408
1	shadyside	0.0000502
2	shadyside	0.0000000
1	shaft	0.0000391
2	shaft	0.0000000
1	shah	0.0000000
2	shah	0.0000273
1	shake	0.0000143
2	shake	0.0000134
1	shaken	0.0000265
2	shaken	0.0000243
1	shakeup	0.0000060
2	shakeup	0.0000192
1	shaking	0.0000367
2	shaking	0.0000367
1	shaky	0.0000144
2	shaky	0.0000211
1	shall	0.0000010
2	shall	0.0000772
1	shallow	0.0000447
2	shallow	0.0000000
1	shame	0.0000005
2	shame	0.0000425
1	shamir	0.0000000
2	shamir	0.0001675
1	shamirs	0.0000000
2	shamirs	0.0000662
1	shamrock	0.0000000
2	shamrock	0.0000584
1	shanker	0.0000000
2	shanker	0.0000273
1	shape	0.0000569
2	shape	0.0000889
1	shaped	0.0000252
2	shaped	0.0000136
1	share	0.0014716
2	share	0.0000988
1	shared	0.0000239
2	shared	0.0000807
1	shareholder	0.0001060
2	shareholder	0.0000000
1	shareholders	0.0002174
2	shareholders	0.0000002
1	shares	0.0009265
2	shares	0.0000195
1	sharing	0.0000702
2	sharing	0.0000211
1	shark	0.0000837
2	shark	0.0000000
1	sharon	0.0000058
2	sharon	0.0000505
1	sharp	0.0001955
2	sharp	0.0000545
1	sharpest	0.0000279
2	sharpest	0.0000039
1	sharpeville	0.0000000
2	sharpeville	0.0000312
1	sharply	0.0003309
2	sharply	0.0000846
1	shatalin	0.0000000
2	shatalin	0.0000234
1	shattered	0.0000447
2	shattered	0.0000000
1	shearson	0.0001005
2	shearson	0.0000000
1	shed	0.0000407
2	shed	0.0000300
1	shedd	0.0000000
2	shedd	0.0000273
1	sheehan	0.0000000
2	sheehan	0.0000273
1	sheet	0.0000447
2	sheet	0.0000000
1	sheets	0.0000335
2	sheets	0.0000000
1	sheftel	0.0000000
2	sheftel	0.0000429
1	sheik	0.0000322
2	sheik	0.0000126
1	shelby	0.0000151
2	shelby	0.0000128
1	shell	0.0000727
2	shell	0.0000272
1	shelling	0.0000208
2	shelling	0.0000089
1	shells	0.0000789
2	shells	0.0000228
1	shelter	0.0000837
2	shelter	0.0000234
1	shelters	0.0000558
2	shelters	0.0000000
1	shelves	0.0000519
2	shelves	0.0000105
1	sheriff	0.0000435
2	sheriff	0.0000203
1	sheriffs	0.0001190
2	sheriffs	0.0000260
1	sherman	0.0000367
2	sherman	0.0000017
1	sherry	0.0000334
2	sherry	0.0000000
1	shes	0.0000383
2	shes	0.0000667
1	shevardnadze	0.0000000
2	shevardnadze	0.0001403
1	shield	0.0000616
2	shield	0.0000622
1	shields	0.0000000
2	shields	0.0000584
1	shift	0.0000794
2	shift	0.0000614
1	shifted	0.0000459
2	shifted	0.0000186
1	shifting	0.0000407
2	shifting	0.0000184
1	shifts	0.0000798
2	shifts	0.0000066
1	shiite	0.0000805
2	shiite	0.0000140
1	shiites	0.0000335
2	shiites	0.0000000
1	shiley	0.0000000
2	shiley	0.0000351
1	shimon	0.0000000
2	shimon	0.0000429
1	shining	0.0000011
2	shining	0.0000732
1	ship	0.0005440
2	ship	0.0000177
1	shipbuilding	0.0000313
2	shipbuilding	0.0000054
1	shipment	0.0001176
2	shipment	0.0000036
1	shipments	0.0001202
2	shipments	0.0000213
1	shipped	0.0000893
2	shipped	0.0000000
1	shipping	0.0001275
2	shipping	0.0000006
1	ships	0.0003124
2	ships	0.0000001
1	shipyard	0.0000335
2	shipyard	0.0000195
1	shirt	0.0000388
2	shirt	0.0000236
1	shirts	0.0000074
2	shirts	0.0000182
1	shock	0.0000239
2	shock	0.0000574
1	shocked	0.0000000
2	shocked	0.0000390
1	shocks	0.0000348
2	shocks	0.0000030
1	shoe	0.0000357
2	shoe	0.0000335
1	shoes	0.0000579
2	shoes	0.0000024
1	shook	0.0000491
2	shook	0.0000125
1	shoot	0.0000321
2	shoot	0.0000750
1	shooting	0.0002118
2	shooting	0.0002730
1	shootings	0.0001451
2	shootings	0.0000000
1	shootout	0.0000446
2	shootout	0.0000000
1	shop	0.0000616
2	shop	0.0000778
1	shoppers	0.0000617
2	shoppers	0.0000037
1	shopping	0.0001648
2	shopping	0.0000330
1	shops	0.0000667
2	shops	0.0000704
1	shore	0.0000607
2	shore	0.0000083
1	shores	0.0000335
2	shores	0.0000000
1	short	0.0001479
2	short	0.0002942
1	shortage	0.0001377
2	shortage	0.0000052
1	shortages	0.0000825
2	shortages	0.0000320
1	shorter	0.0000368
2	shorter	0.0000094
1	shortfall	0.0000310
2	shortfall	0.0000173
1	shortly	0.0002122
2	shortly	0.0002259
1	shortterm	0.0001329
2	shortterm	0.0000124
1	shot	0.0004489
2	shot	0.0004970
1	shotgun	0.0000074
2	shotgun	0.0000221
1	shotguns	0.0000272
2	shotguns	0.0000083
1	shots	0.0001466
2	shots	0.0000613
1	shoulder	0.0000272
2	shoulder	0.0000277
1	shoulders	0.0000043
2	shoulders	0.0000476
1	shouldnt	0.0000183
2	shouldnt	0.0000690
1	shouted	0.0000078
2	shouted	0.0001114
1	shouting	0.0000000
2	shouting	0.0000818
1	show	0.0007380
2	show	0.0006498
1	showcase	0.0000244
2	showcase	0.0000064
1	showdown	0.0000245
2	showdown	0.0000296
1	showed	0.0004495
2	showed	0.0002551
1	shower	0.0000335
2	shower	0.0000000
1	showers	0.0001730
2	showers	0.0000000
1	showing	0.0002049
2	showing	0.0001453
1	shown	0.0001470
2	shown	0.0000961
1	shows	0.0002892
2	shows	0.0001215
1	shrine	0.0000246
2	shrine	0.0000257
1	shrink	0.0000333
2	shrink	0.0000001
1	shrinking	0.0000000
2	shrinking	0.0000273
1	shrugged	0.0000276
2	shrugged	0.0000080
1	shultz	0.0000000
2	shultz	0.0001597
1	shuster	0.0000936
2	shuster	0.0000009
1	shut	0.0001532
2	shut	0.0000840
1	shuttle	0.0004409
2	shuttle	0.0000000
1	shuttles	0.0000893
2	shuttles	0.0000000
1	shy	0.0000142
2	shy	0.0000213
1	siblings	0.0000115
2	siblings	0.0000153
1	sick	0.0000514
2	sick	0.0000498
1	sickness	0.0000322
2	sickness	0.0000165
1	side	0.0002244
2	side	0.0005057
1	sides	0.0000499
2	sides	0.0003587
1	sidewalk	0.0000064
2	sidewalk	0.0000423
1	sidon	0.0000391
2	sidon	0.0000000
1	sieck	0.0000447
2	sieck	0.0000000
1	siege	0.0000309
2	siege	0.0000252
1	siegelman	0.0000000
2	siegelman	0.0000351
1	sierra	0.0000493
2	sierra	0.0000007
1	sight	0.0000233
2	sight	0.0000344
1	sigmond	0.0000447
2	sigmond	0.0000000
1	sign	0.0001951
2	sign	0.0002573
1	signal	0.0001624
2	signal	0.0000503
1	signaled	0.0000380
2	signaled	0.0000202
1	signals	0.0001401
2	signals	0.0000269
1	signature	0.0000000
2	signature	0.0000429
1	signatures	0.0000000
2	signatures	0.0000506
1	signed	0.0001141
2	signed	0.0005009
1	significance	0.0000577
2	significance	0.0000299
1	significant	0.0001962
2	significant	0.0002175
1	significantly	0.0000794
2	significantly	0.0000342
1	signing	0.0000000
2	signing	0.0001130
1	signs	0.0001924
2	signs	0.0001307
1	sikh	0.0001226
2	sikh	0.0000001
1	sikhs	0.0000532
2	sikhs	0.0000174
1	silence	0.0000407
2	silence	0.0000339
1	silent	0.0000088
2	silent	0.0000328
1	silicon	0.0000502
2	silicon	0.0000000
1	silk	0.0000000
2	silk	0.0000273
1	silver	0.0002877
2	silver	0.0000174
1	silverado	0.0000000
2	silverado	0.0000506
1	silverman	0.0000000
2	silverman	0.0000273
1	similarities	0.0000271
2	similarities	0.0000044
1	similarly	0.0000314
2	similarly	0.0000132
1	simon	0.0000000
2	simon	0.0001792
1	simons	0.0000000
2	simons	0.0000273
1	simple	0.0000676
2	simple	0.0000736
1	simpler	0.0000176
2	simpler	0.0000111
1	simply	0.0001014
2	simply	0.0001708
1	simpson	0.0000000
2	simpson	0.0000584
1	simultaneously	0.0000100
2	simultaneously	0.0000242
1	sinai	0.0000665
2	sinai	0.0000159
1	sing	0.0000224
2	sing	0.0000350
1	singapore	0.0000476
2	singapore	0.0000057
1	singer	0.0001380
2	singer	0.0001102
1	singers	0.0000447
2	singers	0.0000000
1	singh	0.0000041
2	singh	0.0000322
1	singing	0.0000781
2	singing	0.0000546
1	single	0.0001751
2	single	0.0002401
1	singled	0.0000000
2	singled	0.0000468
1	sinhalese	0.0001340
2	sinhalese	0.0000000
1	sink	0.0000502
2	sink	0.0000000
1	sinking	0.0000502
2	sinking	0.0000000
1	sioux	0.0000277
2	sioux	0.0000119
1	sipan	0.0000335
2	sipan	0.0000000
1	sipc	0.0000781
2	sipc	0.0000000
1	sir	0.0000087
2	sir	0.0000718
1	sister	0.0000541
2	sister	0.0001337
1	sisters	0.0000007
2	sisters	0.0000307
1	sisulu	0.0000000
2	sisulu	0.0000312
1	sit	0.0000727
2	sit	0.0000817
1	site	0.0004351
2	site	0.0000898
1	sites	0.0001537
2	sites	0.0000213
1	sits	0.0000457
2	sits	0.0000110
1	sitting	0.0000948
2	sitting	0.0000702
1	situation	0.0002781
2	situation	0.0004370
1	situations	0.0000608
2	situations	0.0000004
1	six	0.0009434
2	six	0.0006428
1	sixday	0.0000000
2	sixday	0.0000273
1	sixmonth	0.0000664
2	sixmonth	0.0000199
1	sixteen	0.0000233
2	sixteen	0.0000110
1	sixth	0.0001234
2	sixth	0.0000229
1	sixyear	0.0000110
2	sixyear	0.0000430
1	size	0.0002400
2	size	0.0000584
1	sizes	0.0000501
2	sizes	0.0000001
1	skeptical	0.0000146
2	skeptical	0.0000327
1	skepticism	0.0000232
2	skepticism	0.0000072
1	ski	0.0000335
2	ski	0.0000000
1	skies	0.0000365
2	skies	0.0000096
1	skill	0.0000000
2	skill	0.0000311
1	skilled	0.0000447
2	skilled	0.0000000
1	skills	0.0000272
2	skills	0.0000550
1	skin	0.0000302
2	skin	0.0000257
1	skinner	0.0000725
2	skinner	0.0000000
1	skins	0.0000726
2	skins	0.0000000
1	skip	0.0000105
2	skip	0.0000161
1	skirt	0.0000012
2	skirt	0.0000226
1	skirts	0.0000000
2	skirts	0.0000312
1	skull	0.0000000
2	skull	0.0000701
1	skunk	0.0000502
2	skunk	0.0000000
1	sky	0.0001004
2	sky	0.0000001
1	skyrocketing	0.0000014
2	skyrocketing	0.0000302
1	skyscraper	0.0000272
2	skyscraper	0.0000044
1	sl	0.0000103
2	sl	0.0000629
1	slack	0.0000325
2	slack	0.0000007
1	slain	0.0000024
2	slain	0.0000996
1	slammed	0.0000558
2	slammed	0.0000000
1	slap	0.0000171
2	slap	0.0000115
1	slapps	0.0000000
2	slapps	0.0000273
1	slash	0.0000149
2	slash	0.0000129
1	slashed	0.0000362
2	slashed	0.0000176
1	slashing	0.0000000
2	slashing	0.0000273
1	slate	0.0000312
2	slate	0.0000133
1	slated	0.0000121
2	slated	0.0000188
1	slaughter	0.0000391
2	slaughter	0.0000000
1	slaying	0.0000008
2	slaying	0.0001124
1	slayings	0.0000000
2	slayings	0.0000623
1	sleep	0.0000364
2	sleep	0.0000837
1	sleeping	0.0000544
2	sleeping	0.0000166
1	slice	0.0000109
2	slice	0.0000158
1	slide	0.0001228
2	slide	0.0000000
1	slides	0.0000390
2	slides	0.0000000
1	slight	0.0000991
2	slight	0.0000360
1	slightly	0.0004390
2	slightly	0.0000286
1	slip	0.0000080
2	slip	0.0000256
1	slipped	0.0001352
2	slipped	0.0000264
1	slogan	0.0000000
2	slogan	0.0000312
1	slogans	0.0000009
2	slogans	0.0000617
1	slope	0.0000323
2	slope	0.0000047
1	slow	0.0001940
2	slow	0.0000594
1	slowdown	0.0000726
2	slowdown	0.0000000
1	slowed	0.0001140
2	slowed	0.0000101
1	slowest	0.0000391
2	slowest	0.0000000
1	slowing	0.0000837
2	slowing	0.0000000
1	slowly	0.0000706
2	slowly	0.0000208
1	sls	0.0000428
2	sls	0.0000130
1	sluggish	0.0001005
2	sluggish	0.0000000
1	slump	0.0000644
2	slump	0.0000018
1	slums	0.0000877
2	slums	0.0000167
1	small	0.0007510
2	small	0.0003212
1	smaller	0.0002785
2	smaller	0.0000627
1	smallest	0.0000711
2	smallest	0.0000049
1	smashed	0.0000653
2	smashed	0.0000051
1	smeal	0.0000000
2	smeal	0.0000273
1	smile	0.0000000
2	smile	0.0000234
1	smiled	0.0000118
2	smiled	0.0000229
1	smiling	0.0000037
2	smiling	0.0000247
1	smith	0.0002187
2	smith	0.0002447
1	smithkline	0.0000335
2	smithkline	0.0000000
1	smiths	0.0000000
2	smiths	0.0000390
1	smog	0.0000893
2	smog	0.0000000
1	smoke	0.0001959
2	smoke	0.0000113
1	smokeless	0.0000335
2	smokeless	0.0000000
1	smokers	0.0000726
2	smokers	0.0000000
1	smoking	0.0002983
2	smoking	0.0000178
1	smuggle	0.0000072
2	smuggle	0.0000261
1	smuggled	0.0000446
2	smuggled	0.0000000
1	smuggler	0.0000041
2	smuggler	0.0000244
1	smugglers	0.0000303
2	smugglers	0.0000100
1	smuggling	0.0000114
2	smuggling	0.0000816
1	snake	0.0000001
2	snake	0.0000272
1	snapped	0.0000254
2	snapped	0.0000213
1	sniper	0.0000391
2	sniper	0.0000000
1	snow	0.0003795
2	snow	0.0000000
1	soared	0.0000893
2	soared	0.0000000
1	soaring	0.0000657
2	soaring	0.0000126
1	socalled	0.0001073
2	socalled	0.0000965
1	soccer	0.0000000
2	soccer	0.0000468
1	social	0.0001268
2	social	0.0005621
1	socialism	0.0000000
2	socialism	0.0000857
1	socialist	0.0000001
2	socialist	0.0001635
1	socialists	0.0000000
2	socialists	0.0000662
1	societe	0.0000446
2	societe	0.0000000
1	societies	0.0000059
2	societies	0.0000193
1	society	0.0001371
2	society	0.0004692
1	societys	0.0000142
2	societys	0.0000213
1	sofaer	0.0000000
2	sofaer	0.0000273
1	soft	0.0000558
2	soft	0.0000312
1	softened	0.0000000
2	softened	0.0000234
1	software	0.0001172
2	software	0.0000000
1	soil	0.0000913
2	soil	0.0000064
1	solar	0.0000893
2	solar	0.0000000
1	sold	0.0008156
2	sold	0.0001125
1	soldier	0.0000926
2	soldier	0.0000717
1	soldiers	0.0000650
2	soldiers	0.0007416
1	sole	0.0000315
2	sole	0.0000404
1	solely	0.0000071
2	solely	0.0000301
1	solid	0.0001176
2	solid	0.0000504
1	solidarity	0.0000000
2	solidarity	0.0002182
1	solis	0.0000000
2	solis	0.0000312
1	solo	0.0000183
2	solo	0.0000379
1	solomon	0.0000080
2	solomon	0.0000528
1	solution	0.0000846
2	solution	0.0000851
1	solutions	0.0000132
2	solutions	0.0000376
1	solve	0.0000088
2	solve	0.0001029
1	solved	0.0000486
2	solved	0.0000090
1	solving	0.0000000
2	solving	0.0000312
1	somebody	0.0000924
2	somebody	0.0000602
1	son	0.0000610
2	son	0.0005223
1	song	0.0000132
2	song	0.0001271
1	songs	0.0000345
2	songs	0.0000344
1	soninlaw	0.0000000
2	soninlaw	0.0000234
1	sonny	0.0000131
2	sonny	0.0000220
1	sons	0.0000811
2	sons	0.0001187
1	sony	0.0000437
2	sony	0.0000162
1	soon	0.0002824
2	soon	0.0003133
1	sooner	0.0000333
2	sooner	0.0000235
1	sophisticated	0.0000634
2	sophisticated	0.0000259
1	sorry	0.0000055
2	sorry	0.0000702
1	sort	0.0000754
2	sort	0.0001110
1	sorts	0.0000126
2	sorts	0.0000185
1	sothebys	0.0001395
2	sothebys	0.0000000
1	sought	0.0001397
2	sought	0.0003505
1	soul	0.0000070
2	soul	0.0000301
1	sound	0.0001578
2	sound	0.0001041
1	sounded	0.0000412
2	sounded	0.0000219
1	sounds	0.0000299
2	sounds	0.0000337
1	soup	0.0000526
2	soup	0.0000022
1	source	0.0001830
2	source	0.0003826
1	sources	0.0001867
2	sources	0.0005320
1	souter	0.0000000
2	souter	0.0001403
1	souters	0.0000000
2	souters	0.0000468
1	south	0.0007604
2	south	0.0017327
1	southcentral	0.0000335
2	southcentral	0.0000000
1	southeast	0.0001896
2	southeast	0.0000586
1	southeastern	0.0000599
2	southeastern	0.0000244
1	southern	0.0008953
2	southern	0.0002556
1	southwell	0.0000000
2	southwell	0.0000545
1	southwest	0.0002686
2	southwest	0.0000696
1	southwestern	0.0000447
2	southwestern	0.0000000
1	sovereignty	0.0000000
2	sovereignty	0.0000468
1	soviet	0.0002518
2	soviet	0.0037163
1	soviets	0.0000820
2	soviets	0.0004960
1	sowan	0.0000288
2	sowan	0.0000071
1	soweto	0.0000000
2	soweto	0.0000273
1	soybean	0.0002791
2	soybean	0.0000000
1	soybeans	0.0002512
2	soybeans	0.0000000
1	space	0.0008815
2	space	0.0000665
1	spacecraft	0.0002121
2	spacecraft	0.0000000
1	spain	0.0000098
2	spain	0.0001918
1	spains	0.0000001
2	spains	0.0000233
1	span	0.0000087
2	span	0.0000212
1	spanish	0.0000253
2	spanish	0.0001304
1	spare	0.0000000
2	spare	0.0000350
1	sparked	0.0000493
2	sparked	0.0000318
1	speak	0.0000000
2	speak	0.0001558
1	speaker	0.0000000
2	speaker	0.0001909
1	speakers	0.0000000
2	speakers	0.0000623
1	speaking	0.0001358
2	speaking	0.0004117
1	speaks	0.0000000
2	speaks	0.0000506
1	spear	0.0000335
2	spear	0.0000000
1	special	0.0003069
2	special	0.0005650
1	specialist	0.0000412
2	specialist	0.0000570
1	specialists	0.0000577
2	specialists	0.0000221
1	specialty	0.0000208
2	specialty	0.0000089
1	species	0.0002456
2	species	0.0000000
1	specific	0.0001112
2	specific	0.0001990
1	specifically	0.0000158
2	specifically	0.0000591
1	specifics	0.0000013
2	specifics	0.0000575
1	specified	0.0000037
2	specified	0.0000364
1	specify	0.0000266
2	specify	0.0000282
1	spectacular	0.0000391
2	spectacular	0.0000000
1	spectators	0.0000246
2	spectators	0.0000841
1	speculate	0.0000391
2	speculate	0.0000000
1	speculated	0.0000472
2	speculated	0.0000021
1	speculation	0.0001665
2	speculation	0.0000863
1	speculative	0.0000568
2	speculative	0.0000071
1	speculators	0.0000447
2	speculators	0.0000000
1	speech	0.0000000
2	speech	0.0006078
1	speeches	0.0000000
2	speeches	0.0001052
1	speed	0.0002425
2	speed	0.0000178
1	speeding	0.0000200
2	speeding	0.0000094
1	speeds	0.0000391
2	speeds	0.0000000
1	speedy	0.0000000
2	speedy	0.0000234
1	spell	0.0000202
2	spell	0.0000327
1	spence	0.0000447
2	spence	0.0000000
1	spencer	0.0000478
2	spencer	0.0000056
1	spend	0.0001995
2	spend	0.0001296
1	spending	0.0002569
2	spending	0.0005337
1	spends	0.0000115
2	spends	0.0000192
1	spent	0.0002353
2	spent	0.0004124
1	spielberg	0.0000085
2	spielberg	0.0000213
1	spill	0.0000447
2	spill	0.0000000
1	spilled	0.0000335
2	spilled	0.0000000
1	spin	0.0000558
2	spin	0.0000000
1	spinal	0.0000000
2	spinal	0.0000429
1	spirit	0.0000094
2	spirit	0.0000714
1	spirits	0.0000000
2	spirits	0.0000312
1	spiritual	0.0000000
2	spiritual	0.0000584
1	spite	0.0000445
2	spite	0.0000118
1	split	0.0000473
2	split	0.0001579
1	spoke	0.0000732
2	spoke	0.0004982
1	spoken	0.0000000
2	spoken	0.0000429
1	spokesman	0.0014150
2	spokesman	0.0010421
1	spokesmen	0.0000229
2	spokesmen	0.0000269
1	spokeswoman	0.0004133
2	spokeswoman	0.0002297
1	sponsor	0.0000018
2	sponsor	0.0000806
1	sponsored	0.0000000
2	sponsored	0.0000818
1	sponsoring	0.0000108
2	sponsoring	0.0000275
1	sponsors	0.0000009
2	sponsors	0.0000695
1	spoor	0.0000335
2	spoor	0.0000000
1	sporadic	0.0000216
2	sporadic	0.0000200
1	sporting	0.0000004
2	sporting	0.0000270
1	sports	0.0001926
2	sports	0.0000954
1	spot	0.0001077
2	spot	0.0000534
1	spotlight	0.0000190
2	spotlight	0.0000179
1	spots	0.0000527
2	spots	0.0000177
1	spotted	0.0001061
2	spotted	0.0000000
1	spouse	0.0000000
2	spouse	0.0000273
1	sprawling	0.0000296
2	sprawling	0.0000183
1	sprayed	0.0000191
2	sprayed	0.0000139
1	spraying	0.0000335
2	spraying	0.0000000
1	spread	0.0003687
2	spread	0.0000894
1	spreading	0.0000400
2	spreading	0.0000188
1	spring	0.0001772
2	spring	0.0001257
1	springfield	0.0000642
2	springfield	0.0000253
1	springs	0.0000843
2	springs	0.0000230
1	sps	0.0000335
2	sps	0.0000000
1	spur	0.0000006
2	spur	0.0000269
1	spurred	0.0000335
2	spurred	0.0000000
1	spy	0.0000123
2	spy	0.0000538
1	squad	0.0000002
2	squad	0.0000505
1	squads	0.0000005
2	squads	0.0000230
1	square	0.0001209
2	square	0.0001766
1	squarefoot	0.0000502
2	squarefoot	0.0000000
1	squaremile	0.0000285
2	squaremile	0.0000073
1	squeezed	0.0000166
2	squeezed	0.0000157
1	sr	0.0000288
2	sr	0.0000033
1	sri	0.0000948
2	sri	0.0000001
1	srinagar	0.0000893
2	srinagar	0.0000000
1	ss	0.0000000
2	ss	0.0000468
1	st	0.0006041
2	st	0.0002329
1	stabbed	0.0000321
2	stabbed	0.0000906
1	stability	0.0000349
2	stability	0.0001198
1	stabilize	0.0000730
2	stabilize	0.0000036
1	stable	0.0001304
2	stable	0.0000337
1	stacked	0.0000196
2	stacked	0.0000214
1	stadium	0.0000158
2	stadium	0.0000903
1	staff	0.0000547
2	staff	0.0005151
1	staffers	0.0000000
2	staffers	0.0000662
1	stage	0.0003325
2	stage	0.0000874
1	staged	0.0000572
2	staged	0.0000419
1	stages	0.0000591
2	stages	0.0000250
1	stake	0.0001771
2	stake	0.0000828
1	stakes	0.0000146
2	stakes	0.0000326
1	stalemate	0.0000124
2	stalemate	0.0000303
1	stalin	0.0000000
2	stalin	0.0000974
1	stalinist	0.0000000
2	stalinist	0.0000312
1	stalins	0.0000000
2	stalins	0.0000468
1	stalled	0.0000217
2	stalled	0.0000589
1	stallone	0.0000000
2	stallone	0.0000390
1	stamp	0.0000095
2	stamp	0.0000207
1	stamps	0.0000670
2	stamps	0.0000000
1	stan	0.0000189
2	stan	0.0000219
1	stance	0.0000176
2	stance	0.0000734
1	stand	0.0001098
2	stand	0.0002935
1	standard	0.0003057
2	standard	0.0000672
1	standards	0.0003268
2	standards	0.0000524
1	standing	0.0000990
2	standing	0.0001257
1	standoff	0.0000502
2	standoff	0.0000000
1	standpoint	0.0000234
2	standpoint	0.0000070
1	stands	0.0000494
2	stands	0.0000941
1	stanford	0.0000210
2	stanford	0.0000204
1	stanley	0.0000628
2	stanley	0.0000457
1	star	0.0001456
2	star	0.0002334
1	stark	0.0000309
2	stark	0.0000057
1	starring	0.0000608
2	starring	0.0000004
1	stars	0.0000749
2	stars	0.0000841
1	start	0.0003912
2	start	0.0002646
1	started	0.0004575
2	started	0.0001910
1	starting	0.0001942
2	starting	0.0000865
1	starts	0.0000932
2	starts	0.0000206
1	stasi	0.0000000
2	stasi	0.0000273
1	state	0.0014613
2	state	0.0031993
1	stated	0.0000586
2	stated	0.0000643
1	statehood	0.0000000
2	statehood	0.0000545
1	statement	0.0005088
2	statement	0.0009850
1	statements	0.0000289
2	statements	0.0002253
1	stateowned	0.0000447
2	stateowned	0.0000000
1	staterun	0.0000340
2	staterun	0.0001049
1	states	0.0013978
2	states	0.0032008
1	statewide	0.0000000
2	statewide	0.0000351
1	stating	0.0000000
2	stating	0.0000273
1	station	0.0004205
2	station	0.0001857
1	stationed	0.0000248
2	stationed	0.0000450
1	stations	0.0002451
2	stations	0.0000900
1	statistical	0.0000502
2	statistical	0.0000000
1	statistics	0.0002199
2	statistics	0.0000141
1	status	0.0000409
2	status	0.0002325
1	statute	0.0000000
2	statute	0.0000351
1	stay	0.0001307
2	stay	0.0002867
1	stayed	0.0000492
2	stayed	0.0000631
1	staying	0.0000267
2	staying	0.0000554
1	stays	0.0000056
2	stays	0.0000195
1	steadily	0.0000400
2	steadily	0.0000071
1	steady	0.0001187
2	steady	0.0000223
1	steal	0.0000308
2	steal	0.0000136
1	stealing	0.0000234
2	stealing	0.0000343
1	stealth	0.0000827
2	stealth	0.0000007
1	steam	0.0000781
2	steam	0.0000000
1	stearns	0.0000482
2	stearns	0.0000014
1	steel	0.0003017
2	steel	0.0000271
1	steelmakers	0.0000335
2	steelmakers	0.0000000
1	steep	0.0000949
2	steep	0.0000000
1	steering	0.0000342
2	steering	0.0000073
1	steiger	0.0000000
2	steiger	0.0000506
1	stein	0.0000390
2	stein	0.0000000
1	stem	0.0000449
2	stem	0.0000193
1	stemmed	0.0000742
2	stemmed	0.0000222
1	stemming	0.0000010
2	stemming	0.0000539
1	stems	0.0000270
2	stems	0.0000201
1	step	0.0001054
2	step	0.0003394
1	stephen	0.0000986
2	stephen	0.0000870
1	stephens	0.0000073
2	stephens	0.0000650
1	stepped	0.0000112
2	stepped	0.0000779
1	stepping	0.0000214
2	stepping	0.0000552
1	steps	0.0000813
2	steps	0.0001926
1	sterling	0.0000287
2	sterling	0.0000072
1	steve	0.0001685
2	steve	0.0000265
1	steven	0.0000719
2	steven	0.0000862
1	stevens	0.0000000
2	stevens	0.0000351
1	stewart	0.0000356
2	stewart	0.0000141
1	stick	0.0000501
2	stick	0.0000235
1	stickers	0.0000000
2	stickers	0.0000234
1	sticks	0.0000195
2	sticks	0.0000293
1	stiff	0.0000238
2	stiff	0.0000457
1	stimulate	0.0000206
2	stimulate	0.0000129
1	sting	0.0000087
2	sting	0.0000485
1	stir	0.0000193
2	stir	0.0000138
1	stock	0.0025173
2	stock	0.0000000
1	stockholders	0.0000949
2	stockholders	0.0000000
1	stockholm	0.0000051
2	stockholm	0.0000315
1	stockindex	0.0000837
2	stockindex	0.0000000
1	stockpile	0.0000219
2	stockpile	0.0000081
1	stocks	0.0008372
2	stocks	0.0000000
1	stole	0.0000399
2	stole	0.0000072
1	stolen	0.0002157
2	stolen	0.0000209
1	stomach	0.0000244
2	stomach	0.0000258
1	stone	0.0000952
2	stone	0.0000387
1	stones	0.0000203
2	stones	0.0000559
1	stood	0.0000785
2	stood	0.0001478
1	stop	0.0001813
2	stop	0.0003760
1	stopped	0.0001636
2	stopped	0.0002365
1	stopping	0.0000000
2	stopping	0.0000390
1	stops	0.0000431
2	stops	0.0000595
1	storage	0.0001499
2	storage	0.0000123
1	store	0.0003975
2	store	0.0000654
1	stored	0.0000781
2	stored	0.0000000
1	storer	0.0000000
2	storer	0.0000623
1	stores	0.0004577
2	stores	0.0000117
1	stories	0.0000258
2	stories	0.0001183
1	storks	0.0000447
2	storks	0.0000000
1	storm	0.0002679
2	storm	0.0000000
1	stormed	0.0000040
2	stormed	0.0000479
1	storms	0.0000447
2	storms	0.0000000
1	stormy	0.0000194
2	stormy	0.0000176
1	story	0.0001268
2	story	0.0002348
1	straight	0.0001512
2	straight	0.0000542
1	strain	0.0000125
2	strain	0.0000225
1	strained	0.0000000
2	strained	0.0000233
1	strains	0.0000175
2	strains	0.0000151
1	strait	0.0000188
2	strait	0.0000375
1	stranded	0.0000558
2	stranded	0.0000000
1	strange	0.0000566
2	strange	0.0000306
1	strangers	0.0000126
2	strangers	0.0000185
1	strategic	0.0000053
2	strategic	0.0002729
1	strategies	0.0000423
2	strategies	0.0000056
1	strategists	0.0000297
2	strategists	0.0000143
1	strategy	0.0000615
2	strategy	0.0001012
1	strauss	0.0000447
2	strauss	0.0000000
1	street	0.0007982
2	street	0.0003117
1	streeters	0.0000502
2	streeters	0.0000000
1	streets	0.0002089
2	streets	0.0001776
1	strength	0.0002929
2	strength	0.0001034
1	strengthen	0.0000570
2	strengthen	0.0000849
1	strengthened	0.0000287
2	strengthened	0.0000423
1	strengthening	0.0000064
2	strengthening	0.0000228
1	stress	0.0000429
2	stress	0.0000402
1	stressed	0.0000262
2	stressed	0.0001142
1	stretch	0.0000455
2	stretch	0.0000111
1	stretched	0.0000329
2	stretched	0.0000043
1	strict	0.0000206
2	strict	0.0000479
1	strictly	0.0000086
2	strictly	0.0000407
1	strife	0.0000199
2	strife	0.0000211
1	strike	0.0006099
2	strike	0.0002249
1	strikers	0.0000523
2	strikers	0.0000414
1	strikes	0.0000133
2	strikes	0.0001816
1	striking	0.0000598
2	striking	0.0000596
1	string	0.0000643
2	string	0.0000330
1	strip	0.0000238
2	strip	0.0001548
1	stripped	0.0000039
2	stripped	0.0000557
1	stroke	0.0000146
2	stroke	0.0000287
1	strong	0.0007081
2	strong	0.0002966
1	stronger	0.0001348
2	stronger	0.0000423
1	strongest	0.0000648
2	strongest	0.0000443
1	stronghold	0.0000206
2	stronghold	0.0000246
1	strongly	0.0000861
2	strongly	0.0000997
1	strongman	0.0000000
2	strongman	0.0000312
1	struck	0.0002606
2	struck	0.0000441
1	structural	0.0000406
2	structural	0.0000418
1	structure	0.0000733
2	structure	0.0001164
1	structures	0.0000731
2	structures	0.0000269
1	struggle	0.0000096
2	struggle	0.0001570
1	struggled	0.0000584
2	struggled	0.0000254
1	struggling	0.0000705
2	struggling	0.0000443
1	stuart	0.0000781
2	stuart	0.0000000
1	stuck	0.0000283
2	stuck	0.0000465
1	student	0.0000057
2	student	0.0004830
1	students	0.0000166
2	students	0.0011182
1	studied	0.0000892
2	studied	0.0000234
1	studies	0.0002330
2	studies	0.0000555
1	studio	0.0000048
2	studio	0.0000278
1	studios	0.0000494
2	studios	0.0000083
1	study	0.0009428
2	study	0.0000821
1	studying	0.0000623
2	studying	0.0000188
1	stuff	0.0000626
2	stuff	0.0000459
1	stuffed	0.0000387
2	stuffed	0.0000120
1	stunned	0.0000101
2	stunned	0.0000202
1	stunt	0.0000447
2	stunt	0.0000000
1	stupid	0.0000196
2	stupid	0.0000409
1	style	0.0000345
2	style	0.0000733
1	styles	0.0000000
2	styles	0.0000234
1	suarez	0.0000000
2	suarez	0.0000234
1	subcommittee	0.0000496
2	subcommittee	0.0002654
1	subcommittees	0.0000000
2	subcommittees	0.0000273
1	subic	0.0000094
2	subic	0.0000285
1	subject	0.0000815
2	subject	0.0002353
1	subjects	0.0000000
2	subjects	0.0000623
1	submarine	0.0000279
2	submarine	0.0000078
1	submarines	0.0000628
2	submarines	0.0000146
1	submit	0.0000238
2	submit	0.0000691
1	submitted	0.0000396
2	submitted	0.0001048
1	submitting	0.0000000
2	submitting	0.0000234
1	subscribers	0.0000426
2	subscribers	0.0000092
1	subsequent	0.0000364
2	subsequent	0.0000369
1	subsequently	0.0000331
2	subsequently	0.0000197
1	subsidiaries	0.0000834
2	subsidiaries	0.0000003
1	subsidiary	0.0003069
2	subsidiary	0.0000000
1	subsidies	0.0000014
2	subsidies	0.0001471
1	subsidize	0.0000000
2	subsidize	0.0000234
1	subsidized	0.0000391
2	subsidized	0.0000000
1	subsidy	0.0000121
2	subsidy	0.0000305
1	substance	0.0000374
2	substance	0.0000362
1	substances	0.0000362
2	substances	0.0000020
1	substantial	0.0001558
2	substantial	0.0000743
1	substantially	0.0000859
2	substantially	0.0000180
1	substitute	0.0000141
2	substitute	0.0000135
1	suburb	0.0000526
2	suburb	0.0000957
1	suburban	0.0000660
2	suburban	0.0000864
1	suburbs	0.0000640
2	suburbs	0.0000060
1	subway	0.0000289
2	subway	0.0000383
1	succeed	0.0000170
2	succeed	0.0000972
1	succeeded	0.0000341
2	succeeded	0.0000464
1	succeeds	0.0000252
2	succeeds	0.0000097
1	success	0.0001278
2	success	0.0001524
1	successes	0.0000000
2	successes	0.0000351
1	successful	0.0000834
2	successful	0.0001249
1	successfully	0.0000777
2	successfully	0.0000276
1	successor	0.0000192
2	successor	0.0000762
1	sudan	0.0000082
2	sudan	0.0000410
1	sudden	0.0000506
2	sudden	0.0000037
1	suddenly	0.0000175
2	suddenly	0.0000696
1	sue	0.0000216
2	sue	0.0000745
1	sued	0.0000033
2	sued	0.0001341
1	suffer	0.0000658
2	suffer	0.0000281
1	suffered	0.0002721
2	suffered	0.0002153
1	suffering	0.0000757
2	suffering	0.0000952
1	suffers	0.0000000
2	suffers	0.0000584
1	sufficient	0.0000674
2	sufficient	0.0000192
1	sufficiently	0.0000079
2	sufficiently	0.0000178
1	sugar	0.0000266
2	sugar	0.0000243
1	suggest	0.0000478
2	suggest	0.0000640
1	suggested	0.0001208
2	suggested	0.0003520
1	suggesting	0.0000000
2	suggesting	0.0000857
1	suggestion	0.0000076
2	suggestion	0.0000648
1	suggestions	0.0000222
2	suggestions	0.0000469
1	suggests	0.0001035
2	suggests	0.0000329
1	suicide	0.0000159
2	suicide	0.0000785
1	suing	0.0000000
2	suing	0.0000428
1	suit	0.0001511
2	suit	0.0002529
1	suitable	0.0000106
2	suitable	0.0000277
1	suitcase	0.0000278
2	suitcase	0.0000313
1	suits	0.0000114
2	suits	0.0001206
1	sula	0.0000335
2	sula	0.0000000
1	sulfur	0.0000335
2	sulfur	0.0000000
1	sullivan	0.0000534
2	sullivan	0.0000757
1	sum	0.0000428
2	sum	0.0000247
1	summary	0.0000435
2	summary	0.0000203
1	summer	0.0006645
2	summer	0.0001518
1	summit	0.0000205
2	summit	0.0005974
1	summoned	0.0000078
2	summoned	0.0000179
1	sun	0.0001474
2	sun	0.0000413
1	sunday	0.0008709
2	sunday	0.0007674
1	sundays	0.0000232
2	sundays	0.0000812
1	sung	0.0000077
2	sung	0.0000219
1	sunk	0.0000335
2	sunk	0.0000000
1	sunny	0.0000472
2	sunny	0.0000060
1	sununu	0.0000000
2	sununu	0.0000857
1	super	0.0000266
2	super	0.0001022
1	supercomputers	0.0000386
2	supercomputers	0.0000042
1	superconducting	0.0000335
2	superconducting	0.0000000
1	superfund	0.0000006
2	superfund	0.0000229
1	superintendent	0.0000577
2	superintendent	0.0000454
1	superior	0.0000567
2	superior	0.0000968
1	superiors	0.0000001
2	superiors	0.0000272
1	supermarket	0.0000441
2	supermarket	0.0000043
1	supermarkets	0.0000335
2	supermarkets	0.0000000
1	superpower	0.0000000
2	superpower	0.0000623
1	superpowers	0.0000085
2	superpowers	0.0000525
1	supersonic	0.0000335
2	supersonic	0.0000000
1	supervise	0.0000200
2	supervise	0.0000250
1	supervised	0.0000106
2	supervised	0.0000199
1	supervision	0.0000000
2	supervision	0.0000506
1	supervisor	0.0000708
2	supervisor	0.0000090
1	supplementary	0.0000000
2	supplementary	0.0000234
1	supplied	0.0000129
2	supplied	0.0000494
1	supplier	0.0000502
2	supplier	0.0000000
1	suppliers	0.0000614
2	suppliers	0.0000000
1	supplies	0.0004731
2	supplies	0.0000438
1	supply	0.0003537
2	supply	0.0000609
1	support	0.0003244
2	support	0.0014254
1	supported	0.0000623
2	supported	0.0002136
1	supporter	0.0000000
2	supporter	0.0000857
1	supporters	0.0000000
2	supporters	0.0004558
1	supporting	0.0000217
2	supporting	0.0001563
1	supports	0.0000000
2	supports	0.0001831
1	supposed	0.0000562
2	supposed	0.0000426
1	supposedly	0.0000000
2	supposedly	0.0000234
1	suppression	0.0000000
2	suppression	0.0000234
1	supremacy	0.0000000
2	supremacy	0.0000273
1	supreme	0.0000000
2	supreme	0.0006506
1	sure	0.0002654
2	sure	0.0002433
1	surely	0.0000000
2	surely	0.0000389
1	surface	0.0001441
2	surface	0.0000046
1	surfaced	0.0000203
2	surfaced	0.0000209
1	surge	0.0001657
2	surge	0.0000051
1	surged	0.0000781
2	surged	0.0000000
1	surgeon	0.0000572
2	surgeon	0.0000224
1	surgeons	0.0000334
2	surgeons	0.0000000
1	surgery	0.0001407
2	surgery	0.0001472
1	surplus	0.0001454
2	surplus	0.0000115
1	surprise	0.0000771
2	surprise	0.0000670
1	surprised	0.0000623
2	surprised	0.0000890
1	surprises	0.0000277
2	surprises	0.0000158
1	surprising	0.0000094
2	surprising	0.0000285
1	surprisingly	0.0000261
2	surprisingly	0.0000091
1	surrender	0.0000095
2	surrender	0.0000635
1	surrendered	0.0000027
2	surrendered	0.0000332
1	surrendering	0.0000335
2	surrendering	0.0000000
1	surrounded	0.0000390
2	surrounded	0.0000741
1	surrounding	0.0000353
2	surrounding	0.0000922
1	surveillance	0.0000047
2	surveillance	0.0000513
1	survey	0.0006374
2	survey	0.0000577
1	surveyed	0.0000534
2	surveyed	0.0000173
1	surveys	0.0001152
2	surveys	0.0000287
1	survival	0.0001008
2	survival	0.0000193
1	survive	0.0000379
2	survive	0.0000554
1	survived	0.0000826
2	survived	0.0000787
1	surviving	0.0000660
2	surviving	0.0000163
1	survivor	0.0000000
2	survivor	0.0000390
1	survivors	0.0001127
2	survivors	0.0000577
1	susan	0.0000540
2	susan	0.0000207
1	susceptible	0.0000379
2	susceptible	0.0000008
1	suspect	0.0000445
2	suspect	0.0001832
1	suspected	0.0001065
2	suspected	0.0001594
1	suspects	0.0000529
2	suspects	0.0000917
1	suspend	0.0000000
2	suspend	0.0000428
1	suspended	0.0000685
2	suspended	0.0001898
1	suspension	0.0000091
2	suspension	0.0000833
1	suspensions	0.0000119
2	suspensions	0.0000151
1	suspicion	0.0000011
2	suspicion	0.0000460
1	suspicious	0.0000558
2	suspicious	0.0000000
1	sustain	0.0000281
2	sustain	0.0000271
1	sustained	0.0000661
2	sustained	0.0000240
1	sverdlovsk	0.0000070
2	sverdlovsk	0.0000185
1	swaggart	0.0000000
2	swaggart	0.0000468
1	swap	0.0000109
2	swap	0.0000197
1	swapo	0.0000000
2	swapo	0.0000506
1	swastikas	0.0000000
2	swastikas	0.0000234
1	swaziland	0.0000186
2	swaziland	0.0000182
1	sweden	0.0000373
2	sweden	0.0001065
1	swedish	0.0000214
2	swedish	0.0000318
1	sweep	0.0000160
2	sweep	0.0000395
1	sweeping	0.0000131
2	sweeping	0.0001039
1	sweet	0.0000903
2	sweet	0.0000149
1	sweetened	0.0000335
2	sweetened	0.0000000
1	swelling	0.0000391
2	swelling	0.0000000
1	swenson	0.0000502
2	swenson	0.0000000
1	swept	0.0000676
2	swept	0.0000502
1	swift	0.0000416
2	swift	0.0000177
1	swim	0.0000558
2	swim	0.0000000
1	swimming	0.0000740
2	swimming	0.0000029
1	swindler	0.0000000
2	swindler	0.0000351
1	swing	0.0000097
2	swing	0.0000478
1	swiss	0.0003029
2	swiss	0.0000574
1	switch	0.0000253
2	switch	0.0000408
1	switched	0.0000026
2	switched	0.0000254
1	switches	0.0000447
2	switches	0.0000000
1	switching	0.0000381
2	switching	0.0000085
1	switzerland	0.0000872
2	switzerland	0.0000677
1	sworn	0.0000000
2	sworn	0.0000701
1	sylvia	0.0000168
2	sylvia	0.0000116
1	symbol	0.0000120
2	symbol	0.0000695
1	symbolic	0.0000000
2	symbolic	0.0000506
1	symbols	0.0000018
2	symbols	0.0000338
1	symchych	0.0000000
2	symchych	0.0000234
1	sympathy	0.0000064
2	sympathy	0.0000501
1	symphony	0.0000000
2	symphony	0.0001169
1	symposium	0.0000066
2	symposium	0.0000188
1	symptoms	0.0000777
2	symptoms	0.0000003
1	synagogue	0.0000000
2	synagogue	0.0000273
1	syndrome	0.0001375
2	syndrome	0.0000170
1	synthetic	0.0000369
2	synthetic	0.0000093
1	syracuse	0.0000081
2	syracuse	0.0000177
1	syria	0.0000226
2	syria	0.0000933
1	syrian	0.0001431
2	syrian	0.0000364
1	syrians	0.0000166
2	syrians	0.0000157
1	system	0.0007901
2	system	0.0009290
1	systems	0.0006154
2	systems	0.0000769
1	t	0.0001140
2	t	0.0000373
1	table	0.0001131
2	table	0.0001548
1	tables	0.0000390
2	tables	0.0000195
1	tactic	0.0000099
2	tactic	0.0000437
1	tactical	0.0000479
2	tactical	0.0000406
1	tactics	0.0000089
2	tactics	0.0000795
1	taewoo	0.0000000
2	taewoo	0.0000390
1	taft	0.0000000
2	taft	0.0000234
1	tag	0.0000335
2	tag	0.0000000
1	tags	0.0000232
2	tags	0.0000072
1	tailored	0.0000004
2	tailored	0.0000348
1	taipei	0.0000447
2	taipei	0.0000000
1	taiwan	0.0000531
2	taiwan	0.0000564
1	taiwanese	0.0000224
2	taiwanese	0.0000194
1	take	0.0008555
2	take	0.0012456
1	taken	0.0005940
2	taken	0.0006023
1	takeoff	0.0000837
2	takeoff	0.0000000
1	takeover	0.0003711
2	takeover	0.0000371
1	takeovers	0.0000558
2	takeovers	0.0000000
1	takes	0.0001215
2	takes	0.0001762
1	takeshita	0.0000000
2	takeshita	0.0001208
1	taking	0.0004284
2	taking	0.0004256
1	tale	0.0000446
2	tale	0.0000000
1	talent	0.0000089
2	talent	0.0000172
1	talk	0.0000576
2	talk	0.0003455
1	talked	0.0000050
2	talked	0.0001562
1	talking	0.0000822
2	talking	0.0002270
1	talks	0.0001248
2	talks	0.0012414
1	tall	0.0000558
2	tall	0.0000000
1	tally	0.0000539
2	tally	0.0000053
1	tamil	0.0001172
2	tamil	0.0000000
1	tamils	0.0000502
2	tamils	0.0000000
1	tampa	0.0000502
2	tampa	0.0000000
1	tampering	0.0000178
2	tampering	0.0000109
1	tank	0.0002525
2	tank	0.0000146
1	tanker	0.0001340
2	tanker	0.0000000
1	tankers	0.0000670
2	tankers	0.0000000
1	tanks	0.0000752
2	tanks	0.0001501
1	tannery	0.0000558
2	tannery	0.0000000
1	tap	0.0000447
2	tap	0.0000000
1	tape	0.0000754
2	tape	0.0000876
1	taped	0.0000000
2	taped	0.0000506
1	tapes	0.0000503
2	tapes	0.0001948
1	taping	0.0000000
2	taping	0.0000351
1	tapped	0.0000000
2	tapped	0.0000429
1	target	0.0001306
2	target	0.0001270
1	targeted	0.0000307
2	targeted	0.0000059
1	targeting	0.0000247
2	targeting	0.0000178
1	targets	0.0000788
2	targets	0.0000852
1	tariffs	0.0000523
2	tariffs	0.0000648
1	tariq	0.0000000
2	tariq	0.0000273
1	tartus	0.0000391
2	tartus	0.0000000
1	tarver	0.0000447
2	tarver	0.0000000
1	task	0.0000327
2	task	0.0001914
1	tasks	0.0000006
2	tasks	0.0000385
1	tass	0.0000000
2	tass	0.0002961
1	taste	0.0000142
2	taste	0.0000251
1	taught	0.0000000
2	taught	0.0001013
1	tawana	0.0000000
2	tawana	0.0000234
1	tax	0.0006590
2	tax	0.0005491
1	taxable	0.0000000
2	taxable	0.0000234
1	taxation	0.0000191
2	taxation	0.0000140
1	taxed	0.0000335
2	taxed	0.0000000
1	taxes	0.0004563
2	taxes	0.0002542
1	taxpayer	0.0000169
2	taxpayer	0.0000155
1	taxpayers	0.0000349
2	taxpayers	0.0001315
1	taylor	0.0001573
2	taylor	0.0000538
1	teach	0.0000000
2	teach	0.0000584
1	teacher	0.0000016
2	teacher	0.0001975
1	teachers	0.0000001
2	teachers	0.0002610
1	teaches	0.0000127
2	teaches	0.0000223
1	teaching	0.0000000
2	teaching	0.0001325
1	team	0.0003749
2	team	0.0002448
1	teams	0.0000875
2	teams	0.0000636
1	teamsters	0.0000000
2	teamsters	0.0000818
1	tear	0.0000403
2	tear	0.0000887
1	tears	0.0000229
2	tears	0.0000464
1	tech	0.0000048
2	tech	0.0000201
1	technical	0.0002383
2	technical	0.0000713
1	technically	0.0000115
2	technically	0.0000231
1	technicians	0.0000525
2	technicians	0.0000062
1	technique	0.0000330
2	technique	0.0000276
1	techniques	0.0000674
2	techniques	0.0000075
1	technologies	0.0000333
2	technologies	0.0000001
1	technology	0.0003537
2	technology	0.0000453
1	ted	0.0000000
2	ted	0.0000662
1	teddy	0.0000001
2	teddy	0.0000272
1	teeley	0.0000000
2	teeley	0.0000234
1	teenage	0.0000218
2	teenage	0.0000315
1	teenager	0.0000311
2	teenager	0.0000329
1	teenagers	0.0000539
2	teenagers	0.0000636
1	teens	0.0000162
2	teens	0.0000198
1	teeth	0.0000429
2	teeth	0.0000441
1	tegucigalpa	0.0000020
2	tegucigalpa	0.0000376
1	tehran	0.0000001
2	tehran	0.0000973
1	teitelbaum	0.0000000
2	teitelbaum	0.0000351
1	tel	0.0000107
2	tel	0.0000510
1	telecharge	0.0000614
2	telecharge	0.0000000
1	telecommunications	0.0000614
2	telecommunications	0.0000000
1	telegraph	0.0001066
2	telegraph	0.0000113
1	telephone	0.0004866
2	telephone	0.0003655
1	telephones	0.0000428
2	telephones	0.0000130
1	telescope	0.0001507
2	telescope	0.0000000
1	telescopes	0.0000391
2	telescopes	0.0000000
1	teletron	0.0000558
2	teletron	0.0000000
1	televised	0.0000072
2	televised	0.0000962
1	television	0.0003038
2	television	0.0007346
1	televisions	0.0000535
2	televisions	0.0000133
1	tell	0.0001616
2	tell	0.0003197
1	teller	0.0000335
2	teller	0.0000000
1	telling	0.0000850
2	telling	0.0001627
1	tells	0.0000356
2	tells	0.0000687
1	temblor	0.0000335
2	temblor	0.0000000
1	temperature	0.0001507
2	temperature	0.0000000
1	temperatures	0.0003740
2	temperatures	0.0000000
1	temple	0.0000529
2	temple	0.0000683
1	tempo	0.0000837
2	tempo	0.0000000
1	temporarily	0.0000535
2	temporarily	0.0000289
1	temporary	0.0001651
2	temporary	0.0001146
1	temptation	0.0000071
2	temptation	0.0000223
1	tempted	0.0000062
2	tempted	0.0000191
1	ten	0.0000708
2	ten	0.0000480
1	tenant	0.0000000
2	tenant	0.0000273
1	tenants	0.0000217
2	tenants	0.0000121
1	tend	0.0000810
2	tend	0.0000330
1	tendency	0.0000500
2	tendency	0.0000041
1	tender	0.0001410
2	tender	0.0000107
1	tenfold	0.0000335
2	tenfold	0.0000000
1	tenn	0.0000631
2	tenn	0.0000261
1	tennessee	0.0000779
2	tennessee	0.0000898
1	tennis	0.0000406
2	tennis	0.0000223
1	tenor	0.0000335
2	tenor	0.0000000
1	tens	0.0000236
2	tens	0.0000731
1	tense	0.0000123
2	tense	0.0000226
1	tension	0.0000000
2	tension	0.0000818
1	tensions	0.0000234
2	tensions	0.0001239
1	tent	0.0000097
2	tent	0.0000477
1	tentative	0.0001041
2	tentative	0.0000053
1	tentatively	0.0000072
2	tentatively	0.0000339
1	tents	0.0000494
2	tents	0.0000006
1	tenure	0.0000000
2	tenure	0.0000234
1	teresa	0.0000090
2	teresa	0.0000560
1	teresas	0.0000001
2	teresas	0.0000233
1	term	0.0000535
2	term	0.0004224
1	terminal	0.0001345
2	terminal	0.0000074
1	terms	0.0001289
2	terms	0.0003503
1	terrible	0.0000059
2	terrible	0.0000777
1	territorial	0.0000000
2	territorial	0.0000390
1	territories	0.0000000
2	territories	0.0001675
1	territory	0.0000463
2	territory	0.0001820
1	terror	0.0000000
2	terror	0.0000740
1	terrorism	0.0000000
2	terrorism	0.0001597
1	terrorist	0.0000047
2	terrorist	0.0001993
1	terrorists	0.0000033
2	terrorists	0.0001458
1	terry	0.0001215
2	terry	0.0000554
1	test	0.0005328
2	test	0.0001619
1	testament	0.0000000
2	testament	0.0000234
1	tested	0.0001496
2	tested	0.0000475
1	testified	0.0000202
2	testified	0.0003677
1	testify	0.0000000
2	testify	0.0001169
1	testifying	0.0000203
2	testifying	0.0000209
1	testimony	0.0000511
2	testimony	0.0004280
1	testing	0.0002328
2	testing	0.0000868
1	tests	0.0004238
2	tests	0.0001133
1	texaco	0.0001674
2	texaco	0.0000000
1	texas	0.0007937
2	texas	0.0002837
1	text	0.0000000
2	text	0.0000468
1	textile	0.0000211
2	textile	0.0000164
1	th	0.0004961
2	th	0.0006783
1	thailand	0.0000779
2	thailand	0.0000080
1	thank	0.0000000
2	thank	0.0000935
1	thanked	0.0000130
2	thanked	0.0000182
1	thanks	0.0000326
2	thanks	0.0000318
1	thanksgiving	0.0000523
2	thanksgiving	0.0000180
1	thatcher	0.0000000
2	thatcher	0.0002182
1	thatchers	0.0000000
2	thatchers	0.0000584
1	thats	0.0004520
2	thats	0.0006429
1	thcentury	0.0000135
2	thcentury	0.0000256
1	theater	0.0001787
2	theater	0.0000856
1	theaters	0.0001010
2	theaters	0.0000191
1	theft	0.0001201
2	theft	0.0000408
1	theirs	0.0000108
2	theirs	0.0000159
1	theme	0.0000000
2	theme	0.0000935
1	themes	0.0000000
2	themes	0.0000351
1	theodore	0.0000220
2	theodore	0.0000197
1	theory	0.0000683
2	theory	0.0000342
1	therapist	0.0000000
2	therapist	0.0000390
1	therapy	0.0000935
2	therapy	0.0000204
1	theres	0.0004284
2	theres	0.0003088
1	theyd	0.0000390
2	theyd	0.0000468
1	theyll	0.0000689
2	theyll	0.0000610
1	theyre	0.0003521
2	theyre	0.0001984
1	theyve	0.0000888
2	theyve	0.0000666
1	thick	0.0000600
2	thick	0.0000049
1	thieves	0.0001005
2	thieves	0.0000000
1	thin	0.0000618
2	thin	0.0000153
1	thing	0.0002300
2	thing	0.0003381
1	things	0.0003283
2	things	0.0004643
1	think	0.0005883
2	think	0.0015218
1	thinking	0.0000494
2	thinking	0.0000980
1	thinks	0.0000240
2	thinks	0.0001041
1	third	0.0005398
2	third	0.0003946
1	thirdquarter	0.0000391
2	thirdquarter	0.0000000
1	thomas	0.0001384
2	thomas	0.0003125
1	thompson	0.0000870
2	thompson	0.0001886
1	thompsons	0.0000259
2	thompsons	0.0000170
1	thornburgh	0.0000000
2	thornburgh	0.0001013
1	thornton	0.0000062
2	thornton	0.0000190
1	thorough	0.0000254
2	thorough	0.0000095
1	thought	0.0002609
2	thought	0.0004257
1	thoughtful	0.0000085
2	thoughtful	0.0000175
1	thoughts	0.0000156
2	thoughts	0.0000241
1	thousand	0.0000197
2	thousand	0.0000798
1	thousands	0.0003262
2	thousands	0.0005671
1	threat	0.0000860
2	threat	0.0003373
1	threaten	0.0000315
2	threaten	0.0000403
1	threatened	0.0001456
2	threatened	0.0001984
1	threatening	0.0000370
2	threatening	0.0001261
1	threatens	0.0000153
2	threatens	0.0000127
1	threats	0.0000075
2	threats	0.0001895
1	three	0.0024577
2	three	0.0016779
1	threeday	0.0000244
2	threeday	0.0000648
1	threehour	0.0000068
2	threehour	0.0000264
1	threejudge	0.0000000
2	threejudge	0.0000468
1	threemember	0.0000000
2	threemember	0.0000351
1	threemonth	0.0000335
2	threemonth	0.0000000
1	threequarters	0.0000333
2	threequarters	0.0000001
1	threeway	0.0000005
2	threeway	0.0000308
1	threeweek	0.0000013
2	threeweek	0.0000264
1	threeyear	0.0001418
2	threeyear	0.0000023
1	threw	0.0000199
2	threw	0.0001030
1	thrift	0.0000405
2	thrift	0.0001120
1	thrifts	0.0000690
2	thrifts	0.0000063
1	thrilled	0.0000000
2	thrilled	0.0000312
1	throat	0.0000148
2	throat	0.0000325
1	throw	0.0000242
2	throw	0.0000961
1	throwing	0.0000237
2	throwing	0.0000419
1	thrown	0.0000186
2	thrown	0.0000650
1	thugs	0.0000000
2	thugs	0.0000390
1	thunderstorms	0.0001842
2	thunderstorms	0.0000000
1	thurmond	0.0000000
2	thurmond	0.0000273
1	thursday	0.0019820
2	thursday	0.0015113
1	thursdays	0.0002780
2	thursdays	0.0000475
1	thwart	0.0000259
2	thwart	0.0000131
1	thyssen	0.0000000
2	thyssen	0.0000234
1	tiananmen	0.0000000
2	tiananmen	0.0000273
1	tibet	0.0000000
2	tibet	0.0000390
1	tibetan	0.0000000
2	tibetan	0.0000273
1	tibetans	0.0000000
2	tibetans	0.0000234
1	ticket	0.0000919
2	ticket	0.0002358
1	ticketron	0.0000781
2	ticketron	0.0000000
1	tickets	0.0001413
2	tickets	0.0000572
1	tide	0.0000130
2	tide	0.0000182
1	tie	0.0000517
2	tie	0.0000458
1	tied	0.0000973
2	tied	0.0000685
1	ties	0.0000000
2	ties	0.0002610
1	tiger	0.0000837
2	tiger	0.0000000
1	tigers	0.0000726
2	tigers	0.0000000
1	tight	0.0000838
2	tight	0.0000389
1	tighten	0.0000765
2	tighten	0.0000012
1	tightening	0.0000557
2	tightening	0.0000001
1	tightly	0.0000000
2	tightly	0.0000273
1	tim	0.0000561
2	tim	0.0000349
1	timber	0.0000335
2	timber	0.0000000
1	timbuktu	0.0000447
2	timbuktu	0.0000000
1	time	0.0019513
2	time	0.0023314
1	times	0.0007976
2	times	0.0006043
1	timesstock	0.0000726
2	timesstock	0.0000000
1	timetable	0.0000000
2	timetable	0.0000429
1	timing	0.0000251
2	timing	0.0000565
1	timothy	0.0000314
2	timothy	0.0000287
1	tiny	0.0000970
2	tiny	0.0000414
1	tip	0.0000526
2	tip	0.0000373
1	tipped	0.0000088
2	tipped	0.0000173
1	tips	0.0000269
2	tips	0.0000046
1	tire	0.0000096
2	tire	0.0000167
1	tired	0.0000984
2	tired	0.0000560
1	tires	0.0000235
2	tires	0.0000109
1	tissue	0.0000000
2	tissue	0.0000429
1	title	0.0000326
2	title	0.0000902
1	titled	0.0000218
2	titled	0.0000237
1	toast	0.0000030
2	toast	0.0000213
1	tobacco	0.0002185
2	tobacco	0.0000111
1	today	0.0020663
2	today	0.0018342
1	todays	0.0002057
2	todays	0.0002460
1	todd	0.0000512
2	todd	0.0000149
1	tokyo	0.0005745
2	tokyo	0.0000432
1	told	0.0005512
2	told	0.0028021
1	tolerate	0.0000239
2	tolerate	0.0000184
1	tolerated	0.0000071
2	tolerated	0.0000223
1	toll	0.0001341
2	toll	0.0000661
1	tom	0.0001338
2	tom	0.0001131
1	tomb	0.0000113
2	tomb	0.0000505
1	tommy	0.0000039
2	tommy	0.0000284
1	tomorrow	0.0000222
2	tomorrow	0.0000663
1	ton	0.0001284
2	ton	0.0000000
1	tone	0.0000214
2	tone	0.0000396
1	tonight	0.0000485
2	tonight	0.0000870
1	tonka	0.0000447
2	tonka	0.0000000
1	tons	0.0006754
2	tons	0.0000000
1	tony	0.0000307
2	tony	0.0000526
1	tool	0.0000273
2	tool	0.0000121
1	tools	0.0000509
2	tools	0.0000151
1	top	0.0006678
2	top	0.0005352
1	topics	0.0000162
2	topics	0.0000394
1	topped	0.0000702
2	topped	0.0000017
1	topping	0.0000188
2	topping	0.0000102
1	topple	0.0000000
2	topple	0.0000273
1	toppled	0.0000172
2	toppled	0.0000503
1	toppling	0.0000137
2	toppling	0.0000138
1	tops	0.0000215
2	tops	0.0000084
1	torch	0.0000117
2	torch	0.0000230
1	tore	0.0000136
2	tore	0.0000217
1	tornadoes	0.0000502
2	tornadoes	0.0000000
1	toronto	0.0000618
2	toronto	0.0000465
1	torres	0.0000391
2	torres	0.0000000
1	torture	0.0000000
2	torture	0.0000779
1	tortured	0.0000000
2	tortured	0.0000273
1	tory	0.0000000
2	tory	0.0000312
1	tosh	0.0000000
2	tosh	0.0000312
1	toshiki	0.0000004
2	toshiki	0.0000270
1	tossed	0.0000502
2	tossed	0.0000000
1	total	0.0009474
2	total	0.0002114
1	totaled	0.0003573
2	totaled	0.0000077
1	totaling	0.0000935
2	totaling	0.0000049
1	totally	0.0000538
2	totally	0.0000988
1	totals	0.0000441
2	totals	0.0000004
1	touch	0.0000121
2	touch	0.0000772
1	touched	0.0000438
2	touched	0.0000474
1	tough	0.0000588
2	tough	0.0001771
1	tougher	0.0000120
2	tougher	0.0000929
1	toughest	0.0000000
2	toughest	0.0000234
1	tour	0.0000729
2	tour	0.0001985
1	toured	0.0000081
2	toured	0.0000255
1	touring	0.0000115
2	touring	0.0000309
1	tourism	0.0000335
2	tourism	0.0000000
1	tourist	0.0000792
2	tourist	0.0000109
1	tourists	0.0000930
2	tourists	0.0000052
1	tournament	0.0000238
2	tournament	0.0000418
1	toussaint	0.0000000
2	toussaint	0.0000468
1	touted	0.0000000
2	touted	0.0000234
1	tow	0.0000404
2	tow	0.0000030
1	tower	0.0001737
2	tower	0.0000151
1	town	0.0005751
2	town	0.0003388
1	towns	0.0001028
2	towns	0.0000802
1	township	0.0000335
2	township	0.0000506
1	townships	0.0000062
2	townships	0.0000424
1	toxic	0.0001165
2	toxic	0.0000083
1	toy	0.0000502
2	toy	0.0000000
1	toys	0.0000558
2	toys	0.0000000
1	trace	0.0000233
2	trace	0.0000149
1	traced	0.0000502
2	traced	0.0000000
1	traces	0.0000342
2	traces	0.0000034
1	track	0.0001556
2	track	0.0000550
1	tracking	0.0000391
2	tracking	0.0000000
1	tracks	0.0000647
2	tracks	0.0000016
1	trade	0.0011612
2	trade	0.0010362
1	traded	0.0003276
2	traded	0.0000090
1	trademark	0.0000157
2	trademark	0.0000280
1	trader	0.0001730
2	trader	0.0000000
1	traders	0.0004130
2	traders	0.0000000
1	trades	0.0001449
2	trades	0.0000118
1	trading	0.0015911
2	trading	0.0000075
1	tradition	0.0000000
2	tradition	0.0000896
1	traditional	0.0000677
2	traditional	0.0001163
1	traditionally	0.0000322
2	traditionally	0.0001022
1	traffic	0.0003739
2	traffic	0.0000001
1	traffickers	0.0000000
2	traffickers	0.0000506
1	trafficking	0.0000015
2	trafficking	0.0001081
1	tragedy	0.0000708
2	tragedy	0.0000441
1	tragic	0.0000020
2	tragic	0.0000337
1	trail	0.0000664
2	trail	0.0000199
1	trailed	0.0000073
2	trailed	0.0000222
1	trailer	0.0000726
2	trailer	0.0000000
1	trailers	0.0000335
2	trailers	0.0000000
1	train	0.0002904
2	train	0.0000856
1	trained	0.0000659
2	trained	0.0000281
1	training	0.0004683
2	training	0.0001133
1	trains	0.0001563
2	trains	0.0000000
1	transaction	0.0001135
2	transaction	0.0000221
1	transactions	0.0000478
2	transactions	0.0000913
1	transatlantic	0.0000447
2	transatlantic	0.0000000
1	transcript	0.0000000
2	transcript	0.0000234
1	transcripts	0.0000000
2	transcripts	0.0000273
1	transfer	0.0000733
2	transfer	0.0001320
1	transferred	0.0000581
2	transferred	0.0000763
1	transfers	0.0000300
2	transfers	0.0000297
1	transform	0.0000000
2	transform	0.0000273
1	transformation	0.0000106
2	transformation	0.0000316
1	transformed	0.0000120
2	transformed	0.0000306
1	transfusion	0.0000335
2	transfusion	0.0000117
1	transfusions	0.0000435
2	transfusions	0.0000047
1	transistor	0.0000614
2	transistor	0.0000000
1	transit	0.0000754
2	transit	0.0000136
1	transition	0.0000475
2	transition	0.0000876
1	transitional	0.0000249
2	transitional	0.0000060
1	translation	0.0000075
2	translation	0.0000259
1	transmission	0.0000893
2	transmission	0.0000000
1	transmitted	0.0000323
2	transmitted	0.0000281
1	transplant	0.0000000
2	transplant	0.0000935
1	transport	0.0001787
2	transport	0.0000233
1	transportation	0.0004320
2	transportation	0.0000530
1	trapped	0.0000828
2	trapped	0.0000396
1	trash	0.0000939
2	trash	0.0000280
1	trauma	0.0000137
2	trauma	0.0000138
1	traumatic	0.0000002
2	traumatic	0.0000271
1	travel	0.0002809
2	travel	0.0002052
1	traveled	0.0000146
2	traveled	0.0000755
1	travelers	0.0001413
2	travelers	0.0000066
1	traveling	0.0000764
2	traveling	0.0000752
1	travell	0.0000502
2	travell	0.0000000
1	travels	0.0000037
2	travels	0.0000403
1	travis	0.0000000
2	travis	0.0000234
1	treason	0.0000000
2	treason	0.0000351
1	treasure	0.0000130
2	treasure	0.0000182
1	treasurer	0.0000119
2	treasurer	0.0000384
1	treasures	0.0000090
2	treasures	0.0000210
1	treasury	0.0004386
2	treasury	0.0000874
1	treasurys	0.0000614
2	treasurys	0.0000000
1	treat	0.0000922
2	treat	0.0000252
1	treated	0.0002443
2	treated	0.0001178
1	treaties	0.0000000
2	treaties	0.0000273
1	treating	0.0000410
2	treating	0.0000142
1	treatment	0.0003404
2	treatment	0.0001754
1	treatments	0.0000171
2	treatments	0.0000154
1	treaty	0.0000000
2	treaty	0.0004753
1	tree	0.0001229
2	tree	0.0000778
1	trees	0.0002548
2	trees	0.0000208
1	trehan	0.0000614
2	trehan	0.0000000
1	tremendous	0.0000184
2	tremendous	0.0000573
1	tremolite	0.0000335
2	tremolite	0.0000000
1	tremor	0.0000335
2	tremor	0.0000000
1	trend	0.0002348
2	trend	0.0000075
1	trends	0.0000502
2	trends	0.0000000
1	trenton	0.0000226
2	trenton	0.0000076
1	trespassing	0.0000335
2	trespassing	0.0000000
1	trial	0.0000000
2	trial	0.0011376
1	trials	0.0000000
2	trials	0.0001052
1	tribal	0.0000086
2	tribal	0.0000329
1	tribe	0.0000110
2	tribe	0.0000196
1	trible	0.0000000
2	trible	0.0000351
1	tribunal	0.0000000
2	tribunal	0.0000545
1	tribune	0.0000351
2	tribune	0.0000379
1	tribute	0.0000090
2	tribute	0.0000210
1	trident	0.0000333
2	trident	0.0000041
1	tried	0.0001692
2	tried	0.0004468
1	tries	0.0000166
2	tries	0.0000196
1	trigger	0.0000265
2	trigger	0.0000516
1	triggered	0.0000762
2	triggered	0.0000325
1	trillion	0.0001335
2	trillion	0.0000003
1	trim	0.0000283
2	trim	0.0000114
1	trip	0.0001243
2	trip	0.0003769
1	triple	0.0000275
2	triple	0.0000120
1	tripled	0.0000390
2	tripled	0.0000000
1	triplewitching	0.0000335
2	triplewitching	0.0000000
1	trips	0.0000300
2	trips	0.0000726
1	tritium	0.0000391
2	tritium	0.0000000
1	triumph	0.0000000
2	triumph	0.0000312
1	troop	0.0000227
2	troop	0.0000933
1	trooper	0.0000146
2	trooper	0.0000287
1	troops	0.0000534
2	troops	0.0010886
1	tropical	0.0001391
2	tropical	0.0000003
1	trotsky	0.0000000
2	trotsky	0.0000312
1	trouble	0.0001613
2	trouble	0.0001056
1	troubled	0.0001133
2	troubled	0.0000612
1	troublemakers	0.0000008
2	troublemakers	0.0000228
1	troubles	0.0000298
2	troubles	0.0000454
1	trout	0.0000446
2	trout	0.0000156
1	trowbridge	0.0000335
2	trowbridge	0.0000000
1	troy	0.0003740
2	troy	0.0000000
1	truce	0.0000066
2	truce	0.0000928
1	truck	0.0003225
2	truck	0.0000009
1	trucks	0.0002004
2	trucks	0.0000082
1	true	0.0001299
2	true	0.0001353
1	truly	0.0000422
2	truly	0.0000640
1	truman	0.0000002
2	truman	0.0000622
1	trump	0.0000504
2	trump	0.0000700
1	trunk	0.0000558
2	trunk	0.0000000
1	trust	0.0002123
2	trust	0.0002843
1	trustee	0.0000000
2	trustee	0.0000584
1	trustees	0.0000105
2	trustees	0.0000745
1	trusts	0.0000162
2	trusts	0.0000276
1	truth	0.0000095
2	truth	0.0001064
1	try	0.0001922
2	try	0.0003372
1	trying	0.0003278
2	trying	0.0005971
1	tube	0.0000191
2	tube	0.0000139
1	tuberculosis	0.0000293
2	tuberculosis	0.0000068
1	tubes	0.0000159
2	tubes	0.0000201
1	tucker	0.0000117
2	tucker	0.0000269
1	tucson	0.0000502
2	tucson	0.0000000
1	tuesday	0.0018513
2	tuesday	0.0014194
1	tuesdays	0.0002337
2	tuesdays	0.0000862
1	tulsa	0.0000498
2	tulsa	0.0000003
1	tumbled	0.0000949
2	tumbled	0.0000000
1	tumbling	0.0000447
2	tumbling	0.0000000
1	tumor	0.0000558
2	tumor	0.0000000
1	tumors	0.0000447
2	tumors	0.0000000
1	tune	0.0000071
2	tune	0.0000340
1	tunnel	0.0001451
2	tunnel	0.0000000
1	turbulence	0.0000391
2	turbulence	0.0000000
1	turf	0.0000171
2	turf	0.0000387
1	turkey	0.0001824
2	turkey	0.0001298
1	turkeys	0.0000726
2	turkeys	0.0000000
1	turkish	0.0000005
2	turkish	0.0001048
1	turmoil	0.0000273
2	turmoil	0.0000316
1	turn	0.0001421
2	turn	0.0003372
1	turned	0.0002653
2	turned	0.0003135
1	turner	0.0000346
2	turner	0.0000654
1	turning	0.0000717
2	turning	0.0000902
1	turnout	0.0000007
2	turnout	0.0000969
1	turns	0.0000607
2	turns	0.0000239
1	turtle	0.0000502
2	turtle	0.0000000
1	turtles	0.0000837
2	turtles	0.0000000
1	tutwiler	0.0000000
2	tutwiler	0.0000312
1	tv	0.0002190
2	tv	0.0002134
1	twelve	0.0000096
2	twelve	0.0000206
1	twenty	0.0000272
2	twenty	0.0000200
1	twice	0.0002305
2	twice	0.0000963
1	twin	0.0001061
2	twin	0.0000000
1	twitchell	0.0000000
2	twitchell	0.0000234
1	two	0.0035964
2	two	0.0036063
1	twoday	0.0000414
2	twoday	0.0000451
1	twohour	0.0000330
2	twohour	0.0000237
1	twomonth	0.0000209
2	twomonth	0.0000127
1	twoterm	0.0000000
2	twoterm	0.0000234
1	twothirds	0.0000521
2	twothirds	0.0000688
1	twoyear	0.0000592
2	twoyear	0.0000522
1	tyler	0.0000173
2	tyler	0.0000230
1	type	0.0001373
2	type	0.0000639
1	types	0.0000674
2	types	0.0000192
1	typical	0.0000528
2	typical	0.0000060
1	typically	0.0000565
2	typically	0.0000034
1	tyson	0.0000236
2	tyson	0.0000225
1	ual	0.0001340
2	ual	0.0000000
1	uaw	0.0001284
2	uaw	0.0000000
1	ugandan	0.0000000
2	ugandan	0.0000234
1	ugly	0.0000000
2	ugly	0.0000390
1	ukrainian	0.0000000
2	ukrainian	0.0000273
1	ulster	0.0000389
2	ulster	0.0000001
1	ultimate	0.0000300
2	ultimate	0.0000687
1	ultimately	0.0000249
2	ultimately	0.0000489
1	ultraviolet	0.0000335
2	ultraviolet	0.0000000
1	umbrella	0.0000000
2	umbrella	0.0000429
1	un	0.0000000
2	un	0.0004909
1	unable	0.0001630
2	unable	0.0001511
1	unacceptable	0.0000198
2	unacceptable	0.0000329
1	unanimous	0.0000000
2	unanimous	0.0000429
1	unanimously	0.0000190
2	unanimously	0.0000647
1	unanswered	0.0000018
2	unanswered	0.0000416
1	unauthorized	0.0000000
2	unauthorized	0.0000623
1	unavailable	0.0000502
2	unavailable	0.0000000
1	unaware	0.0000138
2	unaware	0.0000332
1	unborn	0.0000000
2	unborn	0.0000351
1	uncertain	0.0000566
2	uncertain	0.0000267
1	uncertainty	0.0000858
2	uncertainty	0.0000219
1	unchanged	0.0004912
2	unchanged	0.0000039
1	uncle	0.0000134
2	uncle	0.0000140
1	unclear	0.0000418
2	unclear	0.0000332
1	uncomfortable	0.0000150
2	uncomfortable	0.0000168
1	unconditional	0.0000000
2	unconditional	0.0000429
1	unconstitutional	0.0000000
2	unconstitutional	0.0000545
1	uncovered	0.0000154
2	uncovered	0.0000282
1	undecided	0.0000251
2	undecided	0.0000176
1	undercover	0.0000277
2	undercover	0.0000586
1	undergo	0.0000163
2	undergo	0.0000315
1	undergoing	0.0000019
2	undergoing	0.0000377
1	undergraduate	0.0000175
2	undergraduate	0.0000112
1	underground	0.0000652
2	underground	0.0000246
1	underlying	0.0000603
2	underlying	0.0000047
1	undermine	0.0000000
2	undermine	0.0000234
1	undermined	0.0000143
2	undermined	0.0000134
1	undermining	0.0000000
2	undermining	0.0000234
1	underscored	0.0000182
2	underscored	0.0000145
1	undersecretary	0.0000204
2	undersecretary	0.0000325
1	understand	0.0000719
2	understand	0.0001991
1	understanding	0.0000143
2	understanding	0.0001030
1	understood	0.0000078
2	understood	0.0000296
1	undertake	0.0000170
2	undertake	0.0000193
1	undertaken	0.0000000
2	undertaken	0.0000545
1	underwent	0.0000455
2	underwent	0.0000267
1	undesirable	0.0000005
2	undesirable	0.0000230
1	undisclosed	0.0000100
2	undisclosed	0.0000398
1	undoubtedly	0.0000187
2	undoubtedly	0.0000104
1	uneasy	0.0000112
2	uneasy	0.0000273
1	unemployed	0.0000079
2	unemployed	0.0000335
1	unemployment	0.0002397
2	unemployment	0.0000314
1	unequivocally	0.0000000
2	unequivocally	0.0000312
1	unexpected	0.0000321
2	unexpected	0.0000321
1	unexpectedly	0.0000255
2	unexpectedly	0.0000056
1	unfair	0.0000410
2	unfair	0.0000727
1	unfairly	0.0000034
2	unfairly	0.0000444
1	unfortunate	0.0000360
2	unfortunate	0.0000177
1	unfortunately	0.0000131
2	unfortunately	0.0000376
1	unhappy	0.0000088
2	unhappy	0.0000289
1	unicef	0.0000391
2	unicef	0.0000000
1	unidentified	0.0000844
2	unidentified	0.0000853
1	unification	0.0000000
2	unification	0.0001714
1	unified	0.0000000
2	unified	0.0000701
1	uniform	0.0000000
2	uniform	0.0000429
1	uniforms	0.0000297
2	uniforms	0.0000338
1	union	0.0005692
2	union	0.0018117
1	unions	0.0002609
2	unions	0.0002504
1	unique	0.0000667
2	unique	0.0000080
1	unisys	0.0000558
2	unisys	0.0000000
1	unit	0.0002325
2	unit	0.0001026
1	unita	0.0000000
2	unita	0.0000545
1	unite	0.0000186
2	unite	0.0000416
1	united	0.0011543
2	united	0.0030825
1	uniteds	0.0000447
2	uniteds	0.0000000
1	units	0.0003800
2	units	0.0000854
1	unity	0.0000000
2	unity	0.0001636
1	universal	0.0000238
2	universal	0.0000885
1	universally	0.0000142
2	universally	0.0000135
1	universities	0.0000281
2	universities	0.0000466
1	university	0.0004336
2	university	0.0008038
1	universitys	0.0000206
2	universitys	0.0000636
1	unix	0.0000614
2	unix	0.0000000
1	unknown	0.0000714
2	unknown	0.0000437
1	unlawful	0.0000000
2	unlawful	0.0000390
1	unleaded	0.0001228
2	unleaded	0.0000000
1	unmanned	0.0000446
2	unmanned	0.0000039
1	unnecessary	0.0000270
2	unnecessary	0.0000357
1	unofficial	0.0000000
2	unofficial	0.0000740
1	unpopular	0.0000000
2	unpopular	0.0000351
1	unprecedented	0.0000529
2	unprecedented	0.0000488
1	unrealistic	0.0000000
2	unrealistic	0.0000273
1	unregulated	0.0000401
2	unregulated	0.0000032
1	unrelated	0.0000278
2	unrelated	0.0000390
1	unrest	0.0000051
2	unrest	0.0001289
1	unrwa	0.0000335
2	unrwa	0.0000000
1	unsolicited	0.0000335
2	unsolicited	0.0000000
1	unspecified	0.0000274
2	unspecified	0.0000705
1	unsuccessful	0.0000000
2	unsuccessful	0.0000506
1	unsuccessfully	0.0000092
2	unsuccessfully	0.0000403
1	unsure	0.0000335
2	unsure	0.0000156
1	unusual	0.0000941
2	unusual	0.0000902
1	unusually	0.0000520
2	unusually	0.0000143
1	unveiled	0.0000426
2	unveiled	0.0000248
1	unwanted	0.0000037
2	unwanted	0.0000208
1	unwilling	0.0000078
2	unwilling	0.0000413
1	upbeat	0.0000136
2	upbeat	0.0000256
1	upcoming	0.0000169
2	upcoming	0.0000700
1	updated	0.0000140
2	updated	0.0000136
1	upgrade	0.0000164
2	upgrade	0.0000119
1	upgraded	0.0000494
2	upgraded	0.0000006
1	upgrading	0.0000335
2	upgrading	0.0000000
1	upham	0.0000391
2	upham	0.0000000
1	upheld	0.0000000
2	upheld	0.0001247
1	uphold	0.0000000
2	uphold	0.0000234
1	upper	0.0001503
2	upper	0.0000237
1	uprising	0.0000005
2	uprising	0.0001750
1	uprisings	0.0000000
2	uprisings	0.0000273
1	upset	0.0000107
2	upset	0.0001016
1	upward	0.0000558
2	upward	0.0000000
1	uranium	0.0000318
2	uranium	0.0000090
1	urban	0.0002168
2	urban	0.0000396
1	urge	0.0000000
2	urge	0.0000506
1	urged	0.0000124
2	urged	0.0003731
1	urgent	0.0000025
2	urgent	0.0000528
1	urges	0.0000000
2	urges	0.0000312
1	urging	0.0000002
2	urging	0.0000972
1	uruguay	0.0000001
2	uruguay	0.0000545
1	usa	0.0000984
2	usa	0.0000443
1	usbacked	0.0000000
2	usbacked	0.0000857
1	usda	0.0001842
2	usda	0.0000000
1	useful	0.0000544
2	useful	0.0000361
1	users	0.0002679
2	users	0.0000000
1	uses	0.0001230
2	uses	0.0000310
1	usg	0.0000558
2	usg	0.0000000
1	usjapan	0.0000164
2	usjapan	0.0000197
1	usled	0.0000017
2	usled	0.0000533
1	uss	0.0001507
2	uss	0.0000000
1	ussoviet	0.0000079
2	ussoviet	0.0000919
1	ussr	0.0000000
2	ussr	0.0000506
1	usual	0.0001202
2	usual	0.0000486
1	uta	0.0001061
2	uta	0.0000000
1	utah	0.0001680
2	utah	0.0000230
1	utilities	0.0002233
2	utilities	0.0000000
1	utility	0.0001266
2	utility	0.0000090
1	v	0.0000223
2	v	0.0000779
1	va	0.0000952
2	va	0.0001167
1	vacant	0.0000218
2	vacant	0.0000393
1	vacated	0.0000078
2	vacated	0.0000179
1	vacation	0.0000618
2	vacation	0.0000737
1	vacationing	0.0000087
2	vacationing	0.0000173
1	vacations	0.0000445
2	vacations	0.0000040
1	vaccine	0.0000390
2	vaccine	0.0000000
1	vague	0.0000000
2	vague	0.0000312
1	valdez	0.0001005
2	valdez	0.0000000
1	valid	0.0000059
2	valid	0.0000310
1	valley	0.0004354
2	valley	0.0000000
1	valleys	0.0000502
2	valleys	0.0000000
1	valuable	0.0000926
2	valuable	0.0000328
1	value	0.0008295
2	value	0.0000405
1	valued	0.0001562
2	valued	0.0000001
1	values	0.0000618
2	values	0.0001127
1	valve	0.0000538
2	valve	0.0000014
1	valves	0.0000510
2	valves	0.0000034
1	van	0.0001746
2	van	0.0000768
1	vance	0.0000175
2	vance	0.0000228
1	vancouver	0.0000391
2	vancouver	0.0000000
1	vandalism	0.0000502
2	vandalism	0.0000000
1	vandenberg	0.0000502
2	vandenberg	0.0000000
1	vanished	0.0000251
2	vanished	0.0000215
1	vanuatu	0.0000000
2	vanuatu	0.0000234
1	vargas	0.0000000
2	vargas	0.0000974
1	varied	0.0000467
2	varied	0.0000142
1	variety	0.0001176
2	variety	0.0000309
1	vary	0.0000404
2	vary	0.0000107
1	varying	0.0000161
2	varying	0.0000199
1	vase	0.0000670
2	vase	0.0000000
1	vast	0.0000430
2	vast	0.0000635
1	vatican	0.0000000
2	vatican	0.0001753
1	vaticans	0.0000000
2	vaticans	0.0000234
1	vegas	0.0001008
2	vegas	0.0000349
1	vegetable	0.0000779
2	vegetable	0.0000002
1	vegetables	0.0000902
2	vegetables	0.0000032
1	vehicle	0.0002332
2	vehicle	0.0000282
1	vehicles	0.0002543
2	vehicles	0.0000212
1	vending	0.0000391
2	vending	0.0000000
1	vendors	0.0000447
2	vendors	0.0000000
1	venezuela	0.0000422
2	venezuela	0.0000095
1	ventura	0.0000000
2	ventura	0.0000234
1	venture	0.0001296
2	venture	0.0000226
1	ventures	0.0000291
2	ventures	0.0000187
1	venus	0.0002344
2	venus	0.0000000
1	verbal	0.0000108
2	verbal	0.0000197
1	verde	0.0000053
2	verde	0.0000314
1	verdict	0.0000000
2	verdict	0.0001714
1	verge	0.0000002
2	verge	0.0000232
1	verification	0.0000000
2	verification	0.0000428
1	verify	0.0000226
2	verify	0.0000076
1	verity	0.0000140
2	verity	0.0000253
1	vermont	0.0000631
2	vermont	0.0000261
1	verne	0.0000447
2	verne	0.0000000
1	vernon	0.0000133
2	vernon	0.0000687
1	version	0.0000869
2	version	0.0001185
1	versions	0.0000133
2	versions	0.0000297
1	vessel	0.0001533
2	vessel	0.0000060
1	vessels	0.0001375
2	vessels	0.0000131
1	veteran	0.0000178
2	veteran	0.0000889
1	veterans	0.0000216
2	veterans	0.0001446
1	veterinarian	0.0000374
2	veterinarian	0.0000012
1	veto	0.0000000
2	veto	0.0001714
1	vetoed	0.0000000
2	vetoed	0.0000506
1	viable	0.0000123
2	viable	0.0000148
1	vice	0.0006209
2	vice	0.0007432
1	victim	0.0000435
2	victim	0.0001956
1	victims	0.0003707
2	victims	0.0004230
1	victor	0.0000371
2	victor	0.0000208
1	victories	0.0000063
2	victories	0.0000501
1	victory	0.0000176
2	victory	0.0003189
1	video	0.0001816
2	video	0.0000135
1	videos	0.0000434
2	videos	0.0000048
1	videotape	0.0000000
2	videotape	0.0000390
1	videotaped	0.0000000
2	videotaped	0.0000234
1	vienna	0.0000159
2	vienna	0.0000357
1	vietnam	0.0000218
2	vietnam	0.0003004
1	vietnamese	0.0000046
2	vietnamese	0.0000669
1	viett	0.0000000
2	viett	0.0000312
1	view	0.0001104
2	view	0.0003203
1	viewed	0.0000622
2	viewed	0.0000695
1	viewers	0.0000203
2	viewers	0.0000638
1	viewing	0.0000335
2	viewing	0.0000000
1	views	0.0000160
2	views	0.0001330
1	vigil	0.0000000
2	vigil	0.0000273
1	vigorously	0.0000141
2	vigorously	0.0000330
1	viguerie	0.0000000
2	viguerie	0.0000234
1	villa	0.0000066
2	villa	0.0000227
1	village	0.0002570
2	village	0.0001050
1	villagers	0.0000387
2	villagers	0.0000158
1	villages	0.0000759
2	villages	0.0000327
1	vincennes	0.0000837
2	vincennes	0.0000000
1	vincent	0.0000275
2	vincent	0.0000081
1	vindicated	0.0000000
2	vindicated	0.0000273
1	vines	0.0000000
2	vines	0.0000429
1	vintners	0.0000335
2	vintners	0.0000000
1	violate	0.0000210
2	violate	0.0000828
1	violated	0.0000310
2	violated	0.0001654
1	violates	0.0000000
2	violates	0.0000545
1	violating	0.0000471
2	violating	0.0000840
1	violation	0.0000046
2	violation	0.0001487
1	violations	0.0002111
2	violations	0.0001253
1	violence	0.0000502
2	violence	0.0006156
1	violent	0.0000090
2	violent	0.0001456
1	violeta	0.0000000
2	violeta	0.0000234
1	virgin	0.0000445
2	virgin	0.0000352
1	virginia	0.0001132
2	virginia	0.0001976
1	virginias	0.0000000
2	virginias	0.0000273
1	virtual	0.0000132
2	virtual	0.0000298
1	virtually	0.0001813
2	virtually	0.0001384
1	virus	0.0003740
2	virus	0.0000000
1	visa	0.0000000
2	visa	0.0000506
1	visas	0.0000000
2	visas	0.0000506
1	visible	0.0000416
2	visible	0.0000177
1	vision	0.0000185
2	vision	0.0000455
1	visit	0.0000120
2	visit	0.0005760
1	visited	0.0000575
2	visited	0.0001975
1	visiting	0.0000162
2	visiting	0.0000627
1	visitor	0.0000000
2	visitor	0.0000234
1	visitors	0.0000623
2	visitors	0.0000461
1	visits	0.0000000
2	visits	0.0001208
1	visual	0.0000335
2	visual	0.0000000
1	vital	0.0000187
2	vital	0.0000999
1	vitamin	0.0000000
2	vitamin	0.0000312
1	vladimir	0.0000000
2	vladimir	0.0000545
1	voa	0.0000000
2	voa	0.0000312
1	vocal	0.0000000
2	vocal	0.0000429
1	vocalist	0.0000213
2	vocalist	0.0000085
1	vogel	0.0000000
2	vogel	0.0000234
1	voice	0.0000296
2	voice	0.0001859
1	voiced	0.0000272
2	voiced	0.0000278
1	voices	0.0000152
2	voices	0.0000284
1	volatile	0.0000699
2	volatile	0.0000018
1	volatility	0.0000391
2	volatility	0.0000000
1	volcano	0.0000335
2	volcano	0.0000000
1	volume	0.0006170
2	volume	0.0000096
1	volumes	0.0000141
2	volumes	0.0000174
1	voluntarily	0.0000312
2	voluntarily	0.0000210
1	voluntary	0.0000399
2	voluntary	0.0000501
1	volunteer	0.0000199
2	volunteer	0.0000524
1	volunteers	0.0000351
2	volunteers	0.0000573
1	volvo	0.0000447
2	volvo	0.0000000
1	von	0.0000000
2	von	0.0000351
1	vote	0.0000000
2	vote	0.0011844
1	voted	0.0000274
2	voted	0.0003705
1	voter	0.0000000
2	voter	0.0000896
1	voters	0.0000000
2	voters	0.0005182
1	votes	0.0000000
2	votes	0.0004286
1	voting	0.0000000
2	voting	0.0002688
1	vowed	0.0000000
2	vowed	0.0000740
1	vremya	0.0000142
2	vremya	0.0000252
1	vs	0.0000249
2	vs	0.0000722
1	vulnerability	0.0000154
2	vulnerability	0.0000165
1	vulnerable	0.0000601
2	vulnerable	0.0000243
1	w	0.0001476
2	w	0.0000840
1	wade	0.0000161
2	wade	0.0000277
1	wage	0.0002333
2	wage	0.0000514
1	waged	0.0000157
2	waged	0.0000436
1	wages	0.0001563
2	wages	0.0000000
1	waging	0.0000001
2	waging	0.0000272
1	wagner	0.0000000
2	wagner	0.0000351
1	wait	0.0000567
2	wait	0.0000773
1	waited	0.0000253
2	waited	0.0000486
1	waiting	0.0001892
2	waiting	0.0001406
1	waiver	0.0000000
2	waiver	0.0000273
1	wake	0.0000457
2	wake	0.0000265
1	waldheim	0.0000000
2	waldheim	0.0000545
1	wales	0.0000290
2	wales	0.0000070
1	walesa	0.0000000
2	walesa	0.0001636
1	walk	0.0000874
2	walk	0.0000948
1	walked	0.0001324
2	walked	0.0001335
1	walker	0.0000000
2	walker	0.0000429
1	walking	0.0001127
2	walking	0.0000616
1	walkout	0.0000833
2	walkout	0.0000197
1	walks	0.0000135
2	walks	0.0000296
1	wall	0.0007861
2	wall	0.0000630
1	wallace	0.0000134
2	wallace	0.0000569
1	wallach	0.0001224
2	wallach	0.0000003
1	wallenda	0.0000000
2	wallenda	0.0000429
1	walls	0.0000446
2	walls	0.0000585
1	walsh	0.0000000
2	walsh	0.0001441
1	walshs	0.0000000
2	walshs	0.0000390
1	walt	0.0000614
2	walt	0.0000000
1	walter	0.0000289
2	walter	0.0001279
1	walters	0.0000067
2	walters	0.0000810
1	wang	0.0000077
2	wang	0.0000219
1	wanted	0.0001500
2	wanted	0.0005888
1	wanting	0.0000293
2	wanting	0.0000185
1	wants	0.0000847
2	wants	0.0004396
1	war	0.0001364
2	war	0.0017710
1	ward	0.0000666
2	ward	0.0000704
1	warehouse	0.0000819
2	warehouse	0.0000169
1	warehouses	0.0000420
2	warehouses	0.0000019
1	waren	0.0000000
2	waren	0.0000234
1	warfare	0.0000100
2	warfare	0.0000359
1	warheads	0.0000000
2	warheads	0.0000468
1	warm	0.0000988
2	warm	0.0000207
1	warming	0.0000216
2	warming	0.0000784
1	warmus	0.0000000
2	warmus	0.0000662
1	warn	0.0000265
2	warn	0.0000205
1	warned	0.0001103
2	warned	0.0002853
1	warner	0.0000010
2	warner	0.0000928
1	warning	0.0002467
2	warning	0.0000927
1	warnings	0.0001429
2	warnings	0.0000288
1	warrant	0.0000082
2	warrant	0.0001229
1	warranted	0.0000223
2	warranted	0.0000117
1	warrants	0.0000828
2	warrants	0.0000357
1	warren	0.0000842
2	warren	0.0000776
1	warring	0.0000115
2	warring	0.0000154
1	wars	0.0000208
2	wars	0.0001647
1	warsaw	0.0000001
2	warsaw	0.0001207
1	warships	0.0000793
2	warships	0.0000109
1	wartime	0.0000068
2	wartime	0.0000303
1	wary	0.0000097
2	wary	0.0000166
1	wash	0.0000587
2	wash	0.0000253
1	washed	0.0000558
2	washed	0.0000000
1	washington	0.0005220
2	washington	0.0011823
1	washingtonbased	0.0000610
2	washingtonbased	0.0000120
1	washingtons	0.0000062
2	washingtons	0.0000308
1	wasnt	0.0002058
2	wasnt	0.0001797
1	waste	0.0002865
2	waste	0.0001390
1	wastes	0.0000549
2	wastes	0.0000007
1	watch	0.0000860
2	watch	0.0001308
1	watched	0.0000907
2	watched	0.0000497
1	watchers	0.0000237
2	watchers	0.0000068
1	watches	0.0000335
2	watches	0.0000000
1	watching	0.0000967
2	watching	0.0001156
1	water	0.0012944
2	water	0.0000004
1	watergate	0.0000000
2	watergate	0.0000351
1	waters	0.0001674
2	waters	0.0000000
1	waterway	0.0000278
2	waterway	0.0000312
1	watkins	0.0000824
2	watkins	0.0000476
1	watson	0.0000335
2	watson	0.0000000
1	wave	0.0001158
2	wave	0.0000478
1	waved	0.0000000
2	waved	0.0000390
1	waves	0.0000628
2	waves	0.0000068
1	waving	0.0000068
2	waving	0.0000264
1	wayne	0.0000463
2	wayne	0.0000261
1	ways	0.0001343
2	ways	0.0002257
1	weak	0.0001847
2	weak	0.0000113
1	weaken	0.0000158
2	weaken	0.0000241
1	weakened	0.0000604
2	weakened	0.0000162
1	weakening	0.0000670
2	weakening	0.0000000
1	weaker	0.0001005
2	weaker	0.0000000
1	weakness	0.0001846
2	weakness	0.0000114
1	wealth	0.0000000
2	wealth	0.0000662
1	wealthy	0.0000219
2	wealthy	0.0001133
1	weapon	0.0000349
2	weapon	0.0001276
1	weaponry	0.0000001
2	weaponry	0.0000350
1	weapons	0.0001440
2	weapons	0.0004877
1	wear	0.0000265
2	wear	0.0000828
1	wearing	0.0000504
2	wearing	0.0000700
1	weather	0.0006530
2	weather	0.0000000
1	webb	0.0000000
2	webb	0.0000701
1	webster	0.0000006
2	webster	0.0000346
1	wed	0.0000226
2	wed	0.0000465
1	wedding	0.0000159
2	wedding	0.0000707
1	wednesday	0.0016056
2	wednesday	0.0014896
1	wednesdays	0.0001089
2	wednesdays	0.0000370
1	wedtech	0.0001116
2	wedtech	0.0000000
1	week	0.0018469
2	week	0.0014497
1	weekend	0.0002394
2	weekend	0.0001874
1	weekends	0.0000000
2	weekends	0.0000234
1	weeklong	0.0000012
2	weeklong	0.0000342
1	weekly	0.0001101
2	weekly	0.0001179
1	weekold	0.0000265
2	weekold	0.0000127
1	weeks	0.0006573
2	weeks	0.0006750
1	weicker	0.0000000
2	weicker	0.0000429
1	weigh	0.0000272
2	weigh	0.0000160
1	weighing	0.0000653
2	weighing	0.0000128
1	weight	0.0001750
2	weight	0.0000259
1	weinstein	0.0000000
2	weinstein	0.0000506
1	welcome	0.0000168
2	welcome	0.0001441
1	welcomed	0.0000110
2	welcomed	0.0000936
1	welcoming	0.0000069
2	welcoming	0.0000186
1	welfare	0.0000000
2	welfare	0.0001403
1	wellknown	0.0000181
2	wellknown	0.0000341
1	wells	0.0001005
2	wells	0.0000000
1	wellstone	0.0000000
2	wellstone	0.0000351
1	wellwishers	0.0000174
2	wellwishers	0.0000151
1	went	0.0006551
2	went	0.0005518
1	werent	0.0000441
2	werent	0.0000510
1	west	0.0012392
2	west	0.0012428
1	western	0.0003965
2	western	0.0004791
1	westerners	0.0000000
2	westerners	0.0000545
1	westernstyle	0.0000000
2	westernstyle	0.0000468
1	westinghouse	0.0000614
2	westinghouse	0.0000000
1	wet	0.0000668
2	wet	0.0000040
1	weve	0.0002723
2	weve	0.0002658
1	whales	0.0000000
2	whales	0.0000273
1	whats	0.0000773
2	whats	0.0001058
1	wheat	0.0003349
2	wheat	0.0000000
1	wheel	0.0000434
2	wheel	0.0000009
1	wheelchair	0.0000000
2	wheelchair	0.0000234
1	wheelchairs	0.0000142
2	wheelchairs	0.0000291
1	whereabouts	0.0000006
2	whereabouts	0.0000229
1	whip	0.0000000
2	whip	0.0000623
1	whirlpool	0.0000558
2	whirlpool	0.0000000
1	whistleblowers	0.0000000
2	whistleblowers	0.0000312
1	white	0.0002070
2	white	0.0014801
1	whites	0.0000084
2	whites	0.0001811
1	wholesale	0.0001061
2	wholesale	0.0000000
1	whos	0.0000471
2	whos	0.0000334
1	wickes	0.0000000
2	wickes	0.0000312
1	wide	0.0002120
2	wide	0.0000429
1	widely	0.0001650
2	widely	0.0001108
1	wider	0.0000212
2	wider	0.0000241
1	widespread	0.0001161
2	widespread	0.0001527
1	widow	0.0000145
2	widow	0.0000522
1	wiese	0.0000502
2	wiese	0.0000000
1	wife	0.0001431
2	wife	0.0007884
1	wifes	0.0000004
2	wifes	0.0000465
1	wilbury	0.0000000
2	wilbury	0.0000273
1	wilburys	0.0000000
2	wilburys	0.0000351
1	wild	0.0001660
2	wild	0.0000010
1	wilder	0.0000002
2	wilder	0.0000349
1	wilderness	0.0000280
2	wilderness	0.0000077
1	wildlife	0.0001898
2	wildlife	0.0000000
1	wildly	0.0000147
2	wildly	0.0000170
1	william	0.0003531
2	william	0.0003769
1	williams	0.0000698
2	williams	0.0002785
1	williamson	0.0000330
2	williamson	0.0000004
1	willie	0.0000003
2	willie	0.0000387
1	willing	0.0001145
2	willing	0.0001577
1	willingness	0.0000072
2	willingness	0.0000378
1	willis	0.0000000
2	willis	0.0000273
1	wilshire	0.0000558
2	wilshire	0.0000000
1	wilson	0.0001675
2	wilson	0.0000779
1	win	0.0000704
2	win	0.0004924
1	wind	0.0002184
2	wind	0.0000150
1	windfall	0.0000174
2	windfall	0.0000307
1	window	0.0000649
2	window	0.0000093
1	windows	0.0001730
2	windows	0.0000000
1	winds	0.0003448
2	winds	0.0000048
1	windy	0.0000335
2	windy	0.0000000
1	wine	0.0002769
2	wine	0.0000054
1	winery	0.0000335
2	winery	0.0000000
1	wines	0.0000558
2	wines	0.0000000
1	wing	0.0000950
2	wing	0.0000817
1	wings	0.0001046
2	wings	0.0000166
1	winner	0.0000256
2	winner	0.0001380
1	winners	0.0000251
2	winners	0.0000604
1	winning	0.0000988
2	winning	0.0001180
1	wins	0.0000000
2	wins	0.0000662
1	winter	0.0002925
2	winter	0.0000530
1	wipe	0.0000382
2	wipe	0.0000123
1	wiped	0.0000406
2	wiped	0.0000106
1	wire	0.0000224
2	wire	0.0000818
1	wis	0.0001283
2	wis	0.0000000
1	wisconsin	0.0000677
2	wisconsin	0.0000891
1	wish	0.0000137
2	wish	0.0001229
1	wished	0.0000246
2	wished	0.0000179
1	wishes	0.0000165
2	wishes	0.0000274
1	withdraw	0.0000188
2	withdraw	0.0001583
1	withdrawal	0.0000000
2	withdrawal	0.0002026
1	withdrawing	0.0000000
2	withdrawing	0.0000467
1	withdrawn	0.0000357
2	withdrawn	0.0000374
1	withdrew	0.0000462
2	withdrew	0.0000457
1	withheld	0.0000277
2	withheld	0.0000664
1	withhold	0.0000000
2	withhold	0.0000234
1	witness	0.0000332
2	witness	0.0001366
1	witnessed	0.0000003
2	witnessed	0.0000271
1	witnesses	0.0001116
2	witnesses	0.0002806
1	witter	0.0000614
2	witter	0.0000000
1	wittman	0.0000334
2	wittman	0.0000001
1	wives	0.0000000
2	wives	0.0000390
1	woes	0.0000250
2	woes	0.0000176
1	wolf	0.0000341
2	wolf	0.0000151
1	wolvovitz	0.0000000
2	wolvovitz	0.0000273
1	woman	0.0001767
2	woman	0.0004688
1	womans	0.0000179
2	womans	0.0000771
1	womb	0.0000040
2	womb	0.0000206
1	women	0.0002283
2	women	0.0009159
1	womens	0.0000273
2	womens	0.0001212
1	won	0.0000922
2	won	0.0007304
1	wonder	0.0000532
2	wonder	0.0000408
1	wonderful	0.0000163
2	wonderful	0.0000276
1	wondering	0.0000110
2	wondering	0.0000157
1	wont	0.0002114
2	wont	0.0002187
1	wood	0.0001716
2	wood	0.0000244
1	wooden	0.0000850
2	wooden	0.0000030
1	woods	0.0000497
2	woods	0.0000666
1	woody	0.0000130
2	woody	0.0000221
1	word	0.0001725
2	word	0.0002146
1	words	0.0000230
2	words	0.0001905
1	wore	0.0000148
2	wore	0.0000403
1	work	0.0011933
2	work	0.0010644
1	worked	0.0003084
2	worked	0.0003886
1	worker	0.0002348
2	worker	0.0000738
1	workers	0.0017661
2	workers	0.0004542
1	workforce	0.0000391
2	workforce	0.0000000
1	working	0.0004273
2	working	0.0004926
1	workplace	0.0000000
2	workplace	0.0000390
1	works	0.0002413
2	works	0.0002017
1	world	0.0006327
2	world	0.0015804
1	worlds	0.0003672
2	worlds	0.0000749
1	worldwide	0.0002183
2	worldwide	0.0000347
1	worn	0.0000000
2	worn	0.0000273
1	worried	0.0000914
2	worried	0.0000882
1	worries	0.0001389
2	worries	0.0000121
1	worry	0.0000877
2	worry	0.0000634
1	worse	0.0001427
2	worse	0.0000485
1	worsened	0.0000335
2	worsened	0.0000000
1	worsening	0.0000271
2	worsening	0.0000044
1	worst	0.0002291
2	worst	0.0000738
1	worth	0.0004831
2	worth	0.0000875
1	worthless	0.0000117
2	worthless	0.0000191
1	wouldbe	0.0000050
2	wouldbe	0.0000315
1	wouldnt	0.0001198
2	wouldnt	0.0002164
1	wound	0.0000250
2	wound	0.0000721
1	wounded	0.0002646
2	wounded	0.0002634
1	wounding	0.0000635
2	wounding	0.0000648
1	wounds	0.0000412
2	wounds	0.0000492
1	wppss	0.0000614
2	wppss	0.0000000
1	wrangling	0.0000000
2	wrangling	0.0000234
1	wrapped	0.0000224
2	wrapped	0.0000350
1	wrath	0.0000134
2	wrath	0.0000140
1	wright	0.0000915
2	wright	0.0001426
1	write	0.0000219
2	write	0.0001094
1	writer	0.0000090
2	writer	0.0001106
1	writers	0.0000004
2	writers	0.0000854
1	writes	0.0000003
2	writes	0.0000426
1	writing	0.0000070
2	writing	0.0001899
1	written	0.0001004
2	written	0.0002689
1	wrong	0.0001006
2	wrong	0.0002259
1	wrongdoing	0.0000000
2	wrongdoing	0.0001052
1	wrote	0.0001122
2	wrote	0.0003970
1	wte	0.0000335
2	wte	0.0000000
1	wva	0.0000305
2	wva	0.0000021
1	wynberg	0.0000000
2	wynberg	0.0000468
1	wyo	0.0000272
2	wyo	0.0000044
1	wyoming	0.0000490
2	wyoming	0.0000282
1	x	0.0000949
2	x	0.0000000
1	xhosas	0.0000000
2	xhosas	0.0000273
1	xinhua	0.0000068
2	xinhua	0.0000303
1	y	0.0000117
2	y	0.0000464
1	yale	0.0000000
2	yale	0.0000701
1	yancy	0.0000335
2	yancy	0.0000000
1	yankee	0.0000084
2	yankee	0.0000175
1	yankees	0.0000000
2	yankees	0.0000468
1	yard	0.0000708
2	yard	0.0001220
1	yards	0.0001760
2	yards	0.0000058
1	yasser	0.0000000
2	yasser	0.0000429
1	yates	0.0000000
2	yates	0.0000740
1	year	0.0057502
2	year	0.0021264
1	yearearlier	0.0000391
2	yearearlier	0.0000000
1	yearend	0.0000305
2	yearend	0.0000021
1	yearlong	0.0000167
2	yearlong	0.0000117
1	yearly	0.0000495
2	yearly	0.0000005
1	yearold	0.0005665
2	yearold	0.0011551
1	years	0.0026432
2	years	0.0033873
1	yegor	0.0000000
2	yegor	0.0000273
1	yelled	0.0000001
2	yelled	0.0000389
1	yellow	0.0001080
2	yellow	0.0000337
1	yellowstone	0.0000670
2	yellowstone	0.0000000
1	yeltsin	0.0000000
2	yeltsin	0.0001480
1	yen	0.0010661
2	yen	0.0000000
1	yesterday	0.0000210
2	yesterday	0.0000360
1	yeutter	0.0000014
2	yeutter	0.0001510
1	yield	0.0002233
2	yield	0.0000000
1	yielded	0.0000270
2	yielded	0.0000162
1	yields	0.0001786
2	yields	0.0000000
1	yitzhak	0.0000000
2	yitzhak	0.0000662
1	york	0.0023544
2	york	0.0007488
1	yorkbased	0.0000413
2	yorkbased	0.0000296
1	yorkers	0.0000128
2	yorkers	0.0000145
1	yorks	0.0001112
2	yorks	0.0000431
1	yosemite	0.0000893
2	yosemite	0.0000000
1	youll	0.0000287
2	youll	0.0000423
1	young	0.0003451
2	young	0.0004175
1	younger	0.0000337
2	younger	0.0000894
1	youngest	0.0000188
2	youngest	0.0000258
1	youngsters	0.0000150
2	youngsters	0.0000402
1	youre	0.0001382
2	youre	0.0002035
1	youth	0.0000021
2	youth	0.0000842
1	youths	0.0000231
2	youths	0.0001280
1	youve	0.0000782
2	youve	0.0000857
1	yugoslav	0.0000168
2	yugoslav	0.0000194
1	yugoslavia	0.0000256
2	yugoslavia	0.0000406
1	yuri	0.0000000
2	yuri	0.0000312
1	zaccaro	0.0000000
2	zaccaro	0.0000506
1	zaire	0.0000000
2	zaire	0.0000273
1	zambia	0.0000000
2	zambia	0.0000390
1	zealand	0.0000220
2	zealand	0.0000197
1	zennoh	0.0000335
2	zennoh	0.0000000
1	zero	0.0000837
2	zero	0.0000117
1	zieman	0.0000000
2	zieman	0.0000234
1	ziemet	0.0000391
2	ziemet	0.0000000
1	zimbabwe	0.0000001
2	zimbabwe	0.0000467
1	zinoviev	0.0000000
2	zinoviev	0.0000234
1	zone	0.0000367
2	zone	0.0000289
1	zones	0.0000007
2	zones	0.0000580
1	zoo	0.0000502
2	zoo	0.0000000
1	zubal	0.0000558
2	zubal	0.0000000
1	zurich	0.0001340
2	zurich	0.0000000

Podemos usar esse resultado para visualizar os principais termos que compõem cada tópico:

ap_top_terms <- ap_topics %>%
  group_by(topic) %>%
  top_n(10, beta) %>%
  ungroup() %>%
  arrange(topic, -beta)

ap_top_terms %>%
  mutate(term = reorder(term, beta)) %>%
  ggplot(aes(term, beta, fill = factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  coord_flip()

O gráfico permite analisar os dois tópicos. As palavras mais comuns no tópico 1 sugerem que ele pode representa notícias na área da economia/finanças. No tópico 2 as palavras mais comuns sugerem que este tópico representa notícias sobre política. É importante constatar que algumas palavras são comuns nos dois tópicos. Essa é uma vantagem da modelagem de tópico, em oposição aos métodos de “hard clustering”: os tópicos obtidos podem ter alguma sobreposição de palavras.

Uma alternativa a essa abordagem é analisar palavras que apresentam as maiores diferenças entre os dois tópicos:

beta_spread <- ap_topics %>%
  mutate(topic = paste0("topic", topic)) %>%
  spread(topic, beta) %>%
  filter(topic1 > .001 | topic2 > .001) %>%
  mutate(log_ratio = log2(topic2 / topic1))

beta_spread %>%
  group_by(direction = log_ratio > 0) %>%
  top_n(10, abs(log_ratio)) %>%
  ungroup() %>%
  mutate(term = reorder(term, log_ratio)) %>%
  ggplot(aes(term, log_ratio)) +
    geom_col() +
  labs(y = "Razão logarítmica de beta no tópico 2 / tópico 1") +
  coord_flip()

Os termos mais comuns no tópico 2 em relação ao tópico 1 incluem “democratic” e “republican”. O tópico 1 foi mais caracterizado por termos referentes a moedas como “yen” e “dollar”, além de termos financeiros como “index”, “prices” e “rates”. Isso ajuda a confirmar que os dois tópicos que o algoritmo identificou referem-se a notícias políticas e financeiras, respectivamente.

9.2.1.1.1.2 Document-topic probabilities

Podemos examinar as probabilidades por-documento-por-tópico, $\gamma$ (Gamma).

ap_documents <- tidy(ap_lda, matrix = "gamma") %>% arrange(document)

document	topic	gamma
1	1	0.2480617
1	2	0.7519383
2	1	0.3615485
2	2	0.6384515
3	1	0.5265844
3	2	0.4734156
4	1	0.3566530
4	2	0.6433470
5	1	0.1812767
5	2	0.8187233
6	1	0.0005883
6	2	0.9994117
7	1	0.7734216
7	2	0.2265784
8	1	0.0044517
8	2	0.9955483
9	1	0.9669915
9	2	0.0330085
10	1	0.1468905
10	2	0.8531095
11	1	0.9949971
11	2	0.0050029
12	1	0.3943949
12	2	0.6056051
13	1	0.0015357
13	2	0.9984643
14	1	0.0085740
14	2	0.9914260
15	1	0.9991472
15	2	0.0008528
16	1	0.7610596
16	2	0.2389404
17	1	0.1436260
17	2	0.8563740
18	1	0.0007197
18	2	0.9992803
19	1	0.9987216
19	2	0.0012784
20	1	0.9993127
20	2	0.0006873
21	1	0.9100520
21	2	0.0899480
22	1	0.9773016
22	2	0.0226984
23	1	0.9668815
23	2	0.0331185
24	1	0.1099811
24	2	0.8900189
25	1	0.8627618
25	2	0.1372382
26	1	0.5162087
26	2	0.4837913
27	1	0.0009150
27	2	0.9990850
28	1	0.7011853
28	2	0.2988147
29	1	0.0062584
29	2	0.9937416
30	1	0.9582044
30	2	0.0417956
31	1	0.9923313
31	2	0.0076687
32	1	0.1399536
32	2	0.8600464
33	1	0.0522818
33	2	0.9477182
34	1	0.1672711
34	2	0.8327289
35	1	0.9990155
35	2	0.0009845
36	1	0.0021262
36	2	0.9978738
37	1	0.1914282
37	2	0.8085718
38	1	0.0013285
38	2	0.9986715
39	1	0.0214163
39	2	0.9785837
40	1	0.9972925
40	2	0.0027075
41	1	0.4773214
41	2	0.5226786
42	1	0.9979285
42	2	0.0020715
43	1	0.4183931
43	2	0.5816069
44	1	0.8777732
44	2	0.1222268
45	1	0.6775602
45	2	0.3224398
46	1	0.0539190
46	2	0.9460810
47	1	0.8530591
47	2	0.1469409
48	1	0.4444607
48	2	0.5555393
49	1	0.5097463
49	2	0.4902537
50	1	0.0007458
50	2	0.9992542
51	1	0.0360226
51	2	0.9639774
52	1	0.9450687
52	2	0.0549313
53	1	0.8420404
53	2	0.1579596
54	1	0.4665631
54	2	0.5334369
55	1	0.1287671
55	2	0.8712329
56	1	0.0206356
56	2	0.9793644
57	1	0.0006689
57	2	0.9993311
58	1	0.9980316
58	2	0.0019684
59	1	0.1519231
59	2	0.8480769
60	1	0.9773082
60	2	0.0226918
61	1	0.9990677
61	2	0.0009323
62	1	0.3781991
62	2	0.6218009
63	1	0.9991301
63	2	0.0008699
64	1	0.2845678
64	2	0.7154322
65	1	0.9933457
65	2	0.0066543
66	1	0.2317115
66	2	0.7682885
67	1	0.9987406
67	2	0.0012594
68	1	0.4572396
68	2	0.5427604
69	1	0.0834359
69	2	0.9165641
70	1	0.6292601
70	2	0.3707399
71	1	0.5498212
71	2	0.4501788
72	1	0.1694890
72	2	0.8305110
73	1	0.9332425
73	2	0.0667575
74	1	0.3830505
74	2	0.6169495
75	1	0.8561268
75	2	0.1438732
76	1	0.4636514
76	2	0.5363486
77	1	0.1515357
77	2	0.8484643
78	1	0.0013888
78	2	0.9986112
79	1	0.7585538
79	2	0.2414462
80	1	0.9993794
80	2	0.0006206
81	1	0.0206160
81	2	0.9793840
82	1	0.2149101
82	2	0.7850899
83	1	0.2174879
83	2	0.7825121
84	1	0.7888177
84	2	0.2111823
85	1	0.4500835
85	2	0.5499165
86	1	0.0008620
86	2	0.9991380
87	1	0.9988507
87	2	0.0011493
88	1	0.9974314
88	2	0.0025686
89	1	0.9961752
89	2	0.0038248
90	1	0.2581354
90	2	0.7418646
91	1	0.3491764
91	2	0.6508236
92	1	0.3766920
92	2	0.6233080
93	1	0.2574773
93	2	0.7425227
94	1	0.0009757
94	2	0.9990243
95	1	0.9379695
95	2	0.0620305
96	1	0.5692897
96	2	0.4307103
97	1	0.0009257
97	2	0.9990743
98	1	0.0116581
98	2	0.9883419
99	1	0.9988266
99	2	0.0011734
100	1	0.9194067
100	2	0.0805933
101	1	0.8844728
101	2	0.1155272
102	1	0.3887441
102	2	0.6112559
103	1	0.0262665
103	2	0.9737335
104	1	0.0099761
104	2	0.9900239
105	1	0.2736442
105	2	0.7263558
106	1	0.9978767
106	2	0.0021233
107	1	0.3985915
107	2	0.6014085
108	1	0.2210856
108	2	0.7789144
109	1	0.6546701
109	2	0.3453299
110	1	0.0722438
110	2	0.9277562
111	1	0.9032346
111	2	0.0967654
112	1	0.2369711
112	2	0.7630289
113	1	0.3650284
113	2	0.6349716
114	1	0.9017092
114	2	0.0982908
115	1	0.9710980
115	2	0.0289020
116	1	0.0306776
116	2	0.9693224
117	1	0.3546299
117	2	0.6453701
118	1	0.1547257
118	2	0.8452743
119	1	0.0010340
119	2	0.9989660
120	1	0.9974425
120	2	0.0025575
121	1	0.9956524
121	2	0.0043476
122	1	0.9924176
122	2	0.0075824
123	1	0.6486324
123	2	0.3513676
124	1	0.3136196
124	2	0.6863804
125	1	0.0590293
125	2	0.9409707
126	1	0.9992427
126	2	0.0007573
127	1	0.9538230
127	2	0.0461770
128	1	0.9931690
128	2	0.0068310
129	1	0.5767072
129	2	0.4232928
130	1	0.3083176
130	2	0.6916824
131	1	0.1556023
131	2	0.8443977
132	1	0.0006735
132	2	0.9993265
133	1	0.9957793
133	2	0.0042207
134	1	0.5515481
134	2	0.4484519
135	1	0.0307737
135	2	0.9692263
136	1	0.0940183
136	2	0.9059817
137	1	0.0008735
137	2	0.9991265
138	1	0.5382346
138	2	0.4617654
139	1	0.1884254
139	2	0.8115746
140	1	0.0006978
140	2	0.9993022
141	1	0.3910119
141	2	0.6089881
142	1	0.2892465
142	2	0.7107535
143	1	0.1647443
143	2	0.8352557
144	1	0.9982984
144	2	0.0017016
145	1	0.7323499
145	2	0.2676501
146	1	0.0617452
146	2	0.9382548
147	1	0.0214560
147	2	0.9785440
148	1	0.9988829
148	2	0.0011171
149	1	0.0103353
149	2	0.9896647
150	1	0.1319244
150	2	0.8680756
151	1	0.8201494
151	2	0.1798506
152	1	0.2346477
152	2	0.7653523
153	1	0.4919328
153	2	0.5080672
154	1	0.9977789
154	2	0.0022211
155	1	0.1127955
155	2	0.8872045
156	1	0.0600884
156	2	0.9399116
157	1	0.1264415
157	2	0.8735585
158	1	0.2487381
158	2	0.7512619
159	1	0.3218358
159	2	0.6781642
160	1	0.2969484
160	2	0.7030516
161	1	0.0024556
161	2	0.9975444
162	1	0.1398311
162	2	0.8601689
163	1	0.3399390
163	2	0.6600610
164	1	0.0586384
164	2	0.9413616
165	1	0.9893986
165	2	0.0106014
166	1	0.2646140
166	2	0.7353860
167	1	0.9990744
167	2	0.0009256
168	1	0.3856370
168	2	0.6143630
169	1	0.3919357
169	2	0.6080643
170	1	0.3421381
170	2	0.6578619
171	1	0.0010312
171	2	0.9989688
172	1	0.0008481
172	2	0.9991519
173	1	0.9953016
173	2	0.0046984
174	1	0.8152985
174	2	0.1847015
175	1	0.0019651
175	2	0.9980349
176	1	0.6193007
176	2	0.3806993
177	1	0.0835090
177	2	0.9164910
178	1	0.4546312
178	2	0.5453688
179	1	0.5545899
179	2	0.4454101
180	1	0.0008809
180	2	0.9991191
181	1	0.0016392
181	2	0.9983608
182	1	0.1637553
182	2	0.8362447
183	1	0.3272954
183	2	0.6727046
184	1	0.9988133
184	2	0.0011867
185	1	0.9389927
185	2	0.0610073
186	1	0.9976792
186	2	0.0023208
187	1	0.6325855
187	2	0.3674145
188	1	0.9989673
188	2	0.0010327
189	1	0.0027867
189	2	0.9972133
190	1	0.3153340
190	2	0.6846660
191	1	0.3530585
191	2	0.6469415
192	1	0.3683133
192	2	0.6316867
193	1	0.2546406
193	2	0.7453594
194	1	0.0270571
194	2	0.9729429
195	1	0.1231639
195	2	0.8768361
196	1	0.0007163
196	2	0.9992837
197	1	0.0013401
197	2	0.9986599
198	1	0.6668280
198	2	0.3331720
199	1	0.0011062
199	2	0.9988938
200	1	0.9969202
200	2	0.0030798
201	1	0.0009928
201	2	0.9990072
202	1	0.5570550
202	2	0.4429450
203	1	0.6389408
203	2	0.3610592
204	1	0.9982287
204	2	0.0017713
205	1	0.9974989
205	2	0.0025011
206	1	0.4947720
206	2	0.5052280
207	1	0.3750229
207	2	0.6249771
208	1	0.0020021
208	2	0.9979979
209	1	0.5132174
209	2	0.4867826
210	1	0.2846652
210	2	0.7153348
211	1	0.0010818
211	2	0.9989182
212	1	0.0030939
212	2	0.9969061
213	1	0.3394620
213	2	0.6605380
214	1	0.4962073
214	2	0.5037927
215	1	0.0006513
215	2	0.9993487
216	1	0.0368004
216	2	0.9631996
217	1	0.4771976
217	2	0.5228024
218	1	0.7121908
218	2	0.2878092
219	1	0.9992630
219	2	0.0007370
220	1	0.3189234
220	2	0.6810766
221	1	0.3007419
221	2	0.6992581
222	1	0.0934429
222	2	0.9065571
223	1	0.2821162
223	2	0.7178838
224	1	0.0006735
224	2	0.9993265
225	1	0.3788830
225	2	0.6211170
226	1	0.4253937
226	2	0.5746063
227	1	0.0108579
227	2	0.9891421
228	1	0.1502709
228	2	0.8497291
229	1	0.9989418
229	2	0.0010582
230	1	0.5309423
230	2	0.4690577
231	1	0.7091679
231	2	0.2908321
232	1	0.0009688
232	2	0.9990312
233	1	0.9988524
233	2	0.0011476
234	1	0.0005802
234	2	0.9994198
235	1	0.7293184
235	2	0.2706816
236	1	0.2381586
236	2	0.7618414
237	1	0.0018575
237	2	0.9981425
238	1	0.9252427
238	2	0.0747573
239	1	0.3048587
239	2	0.6951413
240	1	0.9982450
240	2	0.0017550
241	1	0.6268778
241	2	0.3731222
242	1	0.9960626
242	2	0.0039374
243	1	0.0007371
243	2	0.9992629
244	1	0.0071379
244	2	0.9928621
245	1	0.0984750
245	2	0.9015250
246	1	0.6695552
246	2	0.3304448
247	1	0.0450338
247	2	0.9549662
248	1	0.2481694
248	2	0.7518306
249	1	0.9985556
249	2	0.0014444
250	1	0.6191763
250	2	0.3808237
251	1	0.2903532
251	2	0.7096468
252	1	0.0048365
252	2	0.9951635
253	1	0.3578115
253	2	0.6421885
254	1	0.3546860
254	2	0.6453140
255	1	0.0006089
255	2	0.9993911
256	1	0.0036215
256	2	0.9963785
257	1	0.9968192
257	2	0.0031808
258	1	0.0259107
258	2	0.9740893
259	1	0.0011100
259	2	0.9988900
260	1	0.3102920
260	2	0.6897080
261	1	0.0228210
261	2	0.9771790
262	1	0.0314967
262	2	0.9685033
263	1	0.7942727
263	2	0.2057273
264	1	0.0011220
264	2	0.9988780
265	1	0.6043275
265	2	0.3956725
266	1	0.9557582
266	2	0.0442418
267	1	0.0470995
267	2	0.9529005
268	1	0.6137156
268	2	0.3862844
269	1	0.9982916
269	2	0.0017084
270	1	0.0006816
270	2	0.9993184
271	1	0.9958347
271	2	0.0041653
272	1	0.5942128
272	2	0.4057872
273	1	0.7633313
273	2	0.2366687
274	1	0.1337399
274	2	0.8662601
275	1	0.0051766
275	2	0.9948234
276	1	0.2926437
276	2	0.7073563
277	1	0.2516821
277	2	0.7483179
278	1	0.0014775
278	2	0.9985225
279	1	0.0406011
279	2	0.9593989
280	1	0.3333345
280	2	0.6666655
281	1	0.8099747
281	2	0.1900253
282	1	0.0465636
282	2	0.9534364
283	1	0.3901377
283	2	0.6098623
284	1	0.1743339
284	2	0.8256661
285	1	0.5907216
285	2	0.4092784
286	1	0.0007373
286	2	0.9992627
287	1	0.0005873
287	2	0.9994127
288	1	0.9527682
288	2	0.0472318
289	1	0.3728898
289	2	0.6271102
290	1	0.4219766
290	2	0.5780234
291	1	0.9994207
291	2	0.0005793
292	1	0.0109531
292	2	0.9890469
293	1	0.9718458
293	2	0.0281542
294	1	0.1774938
294	2	0.8225062
295	1	0.9983258
295	2	0.0016742
296	1	0.0008986
296	2	0.9991014
297	1	0.9988831
297	2	0.0011169
298	1	0.0011062
298	2	0.9988938
299	1	0.9988141
299	2	0.0011859
300	1	0.9989667
300	2	0.0010333
301	1	0.1682277
301	2	0.8317723
302	1	0.9984488
302	2	0.0015512
303	1	0.0014622
303	2	0.9985378
304	1	0.3752543
304	2	0.6247457
305	1	0.9985602
305	2	0.0014398
306	1	0.0021988
306	2	0.9978012
307	1	0.0015219
307	2	0.9984781
308	1	0.7446171
308	2	0.2553829
309	1	0.0029254
309	2	0.9970746
310	1	0.7979281
310	2	0.2020719
311	1	0.7237182
311	2	0.2762818
312	1	0.0018044
312	2	0.9981956
313	1	0.2486224
313	2	0.7513776
314	1	0.1487029
314	2	0.8512971
315	1	0.8318201
315	2	0.1681799
316	1	0.9737292
316	2	0.0262708
317	1	0.4132525
317	2	0.5867475
318	1	0.9985311
318	2	0.0014689
319	1	0.2206848
319	2	0.7793152
320	1	0.8307605
320	2	0.1692395
321	1	0.0017141
321	2	0.9982859
322	1	0.0013316
322	2	0.9986684
323	1	0.2310635
323	2	0.7689365
324	1	0.0015751
324	2	0.9984249
325	1	0.5474428
325	2	0.4525572
326	1	0.0446712
326	2	0.9553288
327	1	0.7419305
327	2	0.2580695
328	1	0.4582329
328	2	0.5417671
329	1	0.2969172
329	2	0.7030828
330	1	0.8368741
330	2	0.1631259
331	1	0.2533076
331	2	0.7466924
332	1	0.9977174
332	2	0.0022826
333	1	0.1453655
333	2	0.8546345
334	1	0.0006392
334	2	0.9993608
335	1	0.1896222
335	2	0.8103778
336	1	0.8320244
336	2	0.1679756
337	1	0.3957144
337	2	0.6042856
338	1	0.9956189
338	2	0.0043811
339	1	0.0447019
339	2	0.9552981
340	1	0.6084282
340	2	0.3915718
341	1	0.8629141
341	2	0.1370859
342	1	0.2058440
342	2	0.7941560
343	1	0.0208085
343	2	0.9791915
344	1	0.0008982
344	2	0.9991018
345	1	0.9727492
345	2	0.0272508
346	1	0.8679387
346	2	0.1320613
347	1	0.6526945
347	2	0.3473055
348	1	0.6161673
348	2	0.3838327
349	1	0.9979291
349	2	0.0020709
350	1	0.0019644
350	2	0.9980356
351	1	0.1951710
351	2	0.8048290
352	1	0.4387221
352	2	0.5612779
353	1	0.5468353
353	2	0.4531647
354	1	0.5433257
354	2	0.4566743
355	1	0.0012080
355	2	0.9987920
356	1	0.1823772
356	2	0.8176228
357	1	0.0230814
357	2	0.9769186
358	1	0.5612807
358	2	0.4387193
359	1	0.9132615
359	2	0.0867385
360	1	0.0551554
360	2	0.9448446
361	1	0.9985240
361	2	0.0014760
362	1	0.1550603
362	2	0.8449397
363	1	0.9877261
363	2	0.0122739
364	1	0.4551750
364	2	0.5448250
365	1	0.6092381
365	2	0.3907619
366	1	0.9977763
366	2	0.0022237
367	1	0.6144258
367	2	0.3855742
368	1	0.4218218
368	2	0.5781782
369	1	0.9993559
369	2	0.0006441
370	1	0.9951051
370	2	0.0048949
371	1	0.9927152
371	2	0.0072848
372	1	0.0137606
372	2	0.9862394
373	1	0.0017143
373	2	0.9982857
374	1	0.1729096
374	2	0.8270904
375	1	0.6682240
375	2	0.3317760
376	1	0.9977566
376	2	0.0022434
377	1	0.3565656
377	2	0.6434344
378	1	0.0009158
378	2	0.9990842
379	1	0.0014056
379	2	0.9985944
380	1	0.8275253
380	2	0.1724747
381	1	0.0972493
381	2	0.9027507
382	1	0.3465747
382	2	0.6534253
383	1	0.6745578
383	2	0.3254422
384	1	0.9930737
384	2	0.0069263
385	1	0.9985471
385	2	0.0014529
386	1	0.0254312
386	2	0.9745688
387	1	0.1543229
387	2	0.8456771
388	1	0.9989625
388	2	0.0010375
389	1	0.7659613
389	2	0.2340387
390	1	0.4410599
390	2	0.5589401
391	1	0.3029494
391	2	0.6970506
392	1	0.6872585
392	2	0.3127415
393	1	0.0231289
393	2	0.9768711
394	1	0.0482300
394	2	0.9517700
395	1	0.0056231
395	2	0.9943769
396	1	0.3657272
396	2	0.6342728
397	1	0.2227227
397	2	0.7772773
398	1	0.0565433
398	2	0.9434567
399	1	0.7252032
399	2	0.2747968
400	1	0.9984892
400	2	0.0015108
401	1	0.3681658
401	2	0.6318342
402	1	0.3766151
402	2	0.6233849
403	1	0.2342824
403	2	0.7657176
404	1	0.2202129
404	2	0.7797871
405	1	0.9984127
405	2	0.0015873
406	1	0.0009748
406	2	0.9990252
407	1	0.0206133
407	2	0.9793867
408	1	0.4449150
408	2	0.5550850
409	1	0.1675822
409	2	0.8324178
410	1	0.6743622
410	2	0.3256378
411	1	0.9991348
411	2	0.0008652
412	1	0.7770959
412	2	0.2229041
413	1	0.9980644
413	2	0.0019356
414	1	0.4922972
414	2	0.5077028
415	1	0.0039217
415	2	0.9960783
416	1	0.0007530
416	2	0.9992470
417	1	0.9993676
417	2	0.0006324
418	1	0.0739875
418	2	0.9260125
419	1	0.8064099
419	2	0.1935901
420	1	0.9991503
420	2	0.0008497
421	1	0.0377896
421	2	0.9622104
422	1	0.9962662
422	2	0.0037338
423	1	0.2825789
423	2	0.7174211
424	1	0.3525827
424	2	0.6474173
425	1	0.0024983
425	2	0.9975017
426	1	0.6188952
426	2	0.3811048
427	1	0.0008042
427	2	0.9991958
428	1	0.4391502
428	2	0.5608498
429	1	0.9552306
429	2	0.0447694
430	1	0.0039939
430	2	0.9960061
431	1	0.6606449
431	2	0.3393551
432	1	0.9991139
432	2	0.0008861
433	1	0.4689944
433	2	0.5310056
434	1	0.9993213
434	2	0.0006787
435	1	0.0130498
435	2	0.9869502
436	1	0.9976735
436	2	0.0023265
437	1	0.0014812
437	2	0.9985188
438	1	0.0009376
438	2	0.9990624
439	1	0.5433371
439	2	0.4566629
440	1	0.0004205
440	2	0.9995795
441	1	0.0015688
441	2	0.9984312
442	1	0.0010418
442	2	0.9989582
443	1	0.2175035
443	2	0.7824965
444	1	0.0335200
444	2	0.9664800
445	1	0.9958434
445	2	0.0041566
446	1	0.5051414
446	2	0.4948586
447	1	0.9806580
447	2	0.0193420
448	1	0.0014873
448	2	0.9985127
449	1	0.8241243
449	2	0.1758757
450	1	0.5474450
450	2	0.4525550
451	1	0.0520302
451	2	0.9479698
452	1	0.2037279
452	2	0.7962721
453	1	0.0118866
453	2	0.9881134
454	1	0.5688674
454	2	0.4311326
455	1	0.1672280
455	2	0.8327720
456	1	0.7476229
456	2	0.2523771
457	1	0.0019808
457	2	0.9980192
458	1	0.0009343
458	2	0.9990657
459	1	0.8919140
459	2	0.1080860
460	1	0.9973899
460	2	0.0026101
461	1	0.8999671
461	2	0.1000329
462	1	0.0897215
462	2	0.9102785
463	1	0.9973849
463	2	0.0026151
464	1	0.6243944
464	2	0.3756056
465	1	0.6392771
465	2	0.3607229
466	1	0.9986102
466	2	0.0013898
467	1	0.1474502
467	2	0.8525498
468	1	0.1361449
468	2	0.8638551
469	1	0.2082066
469	2	0.7917934
470	1	0.4004383
470	2	0.5995617
471	1	0.0008279
471	2	0.9991721
472	1	0.5968425
472	2	0.4031575
473	1	0.9970868
473	2	0.0029132
474	1	0.0850822
474	2	0.9149178
475	1	0.9458587
475	2	0.0541413
476	1	0.2747197
476	2	0.7252803
477	1	0.9285594
477	2	0.0714406
478	1	0.7391715
478	2	0.2608285
479	1	0.9981069
479	2	0.0018931
480	1	0.3385849
480	2	0.6614151
481	1	0.7161968
481	2	0.2838032
482	1	0.9988759
482	2	0.0011241
483	1	0.9982716
483	2	0.0017284
484	1	0.2485482
484	2	0.7514518
485	1	0.9978616
485	2	0.0021384
486	1	0.8729072
486	2	0.1270928
487	1	0.4483004
487	2	0.5516996
488	1	0.9990072
488	2	0.0009928
489	1	0.4866774
489	2	0.5133226
490	1	0.4368975
490	2	0.5631025
491	1	0.8268125
491	2	0.1731875
492	1	0.9980571
492	2	0.0019429
493	1	0.6839229
493	2	0.3160771
494	1	0.0011801
494	2	0.9988199
495	1	0.2479406
495	2	0.7520594
496	1	0.2821656
496	2	0.7178344
497	1	0.6437427
497	2	0.3562573
498	1	0.0015936
498	2	0.9984064
499	1	0.7276911
499	2	0.2723089
500	1	0.3305125
500	2	0.6694875
501	1	0.8297753
501	2	0.1702247
502	1	0.0008830
502	2	0.9991170
503	1	0.5674276
503	2	0.4325724
504	1	0.5616765
504	2	0.4383235
505	1	0.3432815
505	2	0.6567185
506	1	0.1559880
506	2	0.8440120
507	1	0.0909371
507	2	0.9090629
508	1	0.7054166
508	2	0.2945834
509	1	0.0020433
509	2	0.9979567
510	1	0.0696803
510	2	0.9303197
511	1	0.5127357
511	2	0.4872643
512	1	0.0318110
512	2	0.9681890
513	1	0.0022255
513	2	0.9977745
514	1	0.0186612
514	2	0.9813388
515	1	0.0042666
515	2	0.9957334
516	1	0.9989784
516	2	0.0010216
517	1	0.0007086
517	2	0.9992914
518	1	0.9802853
518	2	0.0197147
519	1	0.7018879
519	2	0.2981121
520	1	0.0031256
520	2	0.9968744
521	1	0.9989195
521	2	0.0010805
522	1	0.0019158
522	2	0.9980842
523	1	0.2380954
523	2	0.7619046
524	1	0.9966169
524	2	0.0033831
525	1	0.8382947
525	2	0.1617053
526	1	0.3484191
526	2	0.6515809
527	1	0.9982756
527	2	0.0017244
528	1	0.7482554
528	2	0.2517446
529	1	0.0026835
529	2	0.9973165
530	1	0.9986440
530	2	0.0013560
531	1	0.2625398
531	2	0.7374602
532	1	0.1416544
532	2	0.8583456
533	1	0.2474682
533	2	0.7525318
534	1	0.2198980
534	2	0.7801020
535	1	0.1418486
535	2	0.8581514
536	1	0.0112687
536	2	0.9887313
537	1	0.6924147
537	2	0.3075853
538	1	0.0763252
538	2	0.9236748
539	1	0.5913956
539	2	0.4086044
540	1	0.1596829
540	2	0.8403171
541	1	0.0006744
541	2	0.9993256
542	1	0.0361317
542	2	0.9638683
543	1	0.3605943
543	2	0.6394057
544	1	0.9982817
544	2	0.0017183
545	1	0.8833678
545	2	0.1166322
546	1	0.5455577
546	2	0.4544423
547	1	0.0013240
547	2	0.9986760
548	1	0.0038726
548	2	0.9961274
549	1	0.9993255
549	2	0.0006745
550	1	0.1771515
550	2	0.8228485
551	1	0.5780097
551	2	0.4219903
552	1	0.9988567
552	2	0.0011433
553	1	0.8719081
553	2	0.1280919
554	1	0.3855474
554	2	0.6144526
555	1	0.0005787
555	2	0.9994213
556	1	0.3858696
556	2	0.6141304
557	1	0.1885872
557	2	0.8114128
558	1	0.2730482
558	2	0.7269518
559	1	0.0291421
559	2	0.9708579
560	1	0.9994475
560	2	0.0005525
561	1	0.9591251
561	2	0.0408749
562	1	0.9984178
562	2	0.0015822
563	1	0.0006112
563	2	0.9993888
564	1	0.0633289
564	2	0.9366711
565	1	0.3756974
565	2	0.6243026
566	1	0.4309362
566	2	0.5690638
567	1	0.3336362
567	2	0.6663638
568	1	0.8006905
568	2	0.1993095
569	1	0.6208531
569	2	0.3791469
570	1	0.4515781
570	2	0.5484219
571	1	0.0011926
571	2	0.9988074
572	1	0.3342920
572	2	0.6657080
573	1	0.6590728
573	2	0.3409272
574	1	0.4117788
574	2	0.5882212
575	1	0.0006329
575	2	0.9993671
576	1	0.0005174
576	2	0.9994826
577	1	0.0274962
577	2	0.9725038
578	1	0.4912976
578	2	0.5087024
579	1	0.9968533
579	2	0.0031467
580	1	0.0340124
580	2	0.9659876
581	1	0.9990250
581	2	0.0009750
582	1	0.5656838
582	2	0.4343162
583	1	0.1229991
583	2	0.8770009
584	1	0.1458905
584	2	0.8541095
585	1	0.8045335
585	2	0.1954665
586	1	0.2658645
586	2	0.7341355
587	1	0.6653881
587	2	0.3346119
588	1	0.8453613
588	2	0.1546387
589	1	0.0014802
589	2	0.9985198
590	1	0.0017198
590	2	0.9982802
591	1	0.0007842
591	2	0.9992158
592	1	0.6059988
592	2	0.3940012
593	1	0.9972384
593	2	0.0027616
594	1	0.8276038
594	2	0.1723962
595	1	0.1352410
595	2	0.8647590
596	1	0.9029947
596	2	0.0970053
597	1	0.0011169
597	2	0.9988831
598	1	0.9750202
598	2	0.0249798
599	1	0.0191372
599	2	0.9808628
600	1	0.9582726
600	2	0.0417274
601	1	0.3957707
601	2	0.6042293
602	1	0.3388988
602	2	0.6611012
603	1	0.0008383
603	2	0.9991617
604	1	0.8564043
604	2	0.1435957
605	1	0.6227145
605	2	0.3772855
606	1	0.6443300
606	2	0.3556700
607	1	0.0005171
607	2	0.9994829
608	1	0.0011888
608	2	0.9988112
609	1	0.5224517
609	2	0.4775483
610	1	0.1625048
610	2	0.8374952
611	1	0.0570106
611	2	0.9429894
612	1	0.4796503
612	2	0.5203497
613	1	0.0007110
613	2	0.9992890
614	1	0.6081727
614	2	0.3918273
615	1	0.2600661
615	2	0.7399339
616	1	0.2458141
616	2	0.7541859
617	1	0.9985106
617	2	0.0014894
618	1	0.5367481
618	2	0.4632519
619	1	0.0389377
619	2	0.9610623
620	1	0.5987929
620	2	0.4012071
621	1	0.0063690
621	2	0.9936310
622	1	0.0966098
622	2	0.9033902
623	1	0.0740902
623	2	0.9259098
624	1	0.3454880
624	2	0.6545120
625	1	0.0750281
625	2	0.9249719
626	1	0.9979917
626	2	0.0020083
627	1	0.9783557
627	2	0.0216443
628	1	0.9007298
628	2	0.0992702
629	1	0.8233357
629	2	0.1766643
630	1	0.2722614
630	2	0.7277386
631	1	0.9597443
631	2	0.0402557
632	1	0.0010799
632	2	0.9989201
633	1	0.4721813
633	2	0.5278187
634	1	0.9983490
634	2	0.0016510
635	1	0.9979871
635	2	0.0020129
636	1	0.9954206
636	2	0.0045794
637	1	0.8673591
637	2	0.1326409
638	1	0.0241777
638	2	0.9758223
639	1	0.0439306
639	2	0.9560694
640	1	0.6613510
640	2	0.3386490
641	1	0.4405340
641	2	0.5594660
642	1	0.9939824
642	2	0.0060176
643	1	0.9982903
643	2	0.0017097
644	1	0.2005878
644	2	0.7994122
645	1	0.0996067
645	2	0.9003933
646	1	0.0007051
646	2	0.9992949
647	1	0.0133837
647	2	0.9866163
648	1	0.6333624
648	2	0.3666376
649	1	0.9985655
649	2	0.0014345
650	1	0.4361778
650	2	0.5638222
651	1	0.0009031
651	2	0.9990969
652	1	0.1048475
652	2	0.8951525
653	1	0.0023957
653	2	0.9976043
654	1	0.0535134
654	2	0.9464866
655	1	0.3344180
655	2	0.6655820
656	1	0.4251475
656	2	0.5748525
657	1	0.0012823
657	2	0.9987177
658	1	0.4080300
658	2	0.5919700
659	1	0.0008103
659	2	0.9991897
660	1	0.0017588
660	2	0.9982412
661	1	0.0011188
661	2	0.9988812
662	1	0.4652354
662	2	0.5347646
663	1	0.4543361
663	2	0.5456639
664	1	0.5445503
664	2	0.4554497
665	1	0.0595732
665	2	0.9404268
666	1	0.1887658
666	2	0.8112342
667	1	0.0959839
667	2	0.9040161
668	1	0.0014638
668	2	0.9985362
669	1	0.4114122
669	2	0.5885878
670	1	0.0017637
670	2	0.9982363
671	1	0.0006883
671	2	0.9993117
672	1	0.6740969
672	2	0.3259031
673	1	0.9992360
673	2	0.0007640
674	1	0.3069307
674	2	0.6930693
675	1	0.0021097
675	2	0.9978903
676	1	0.3594524
676	2	0.6405476
677	1	0.9944932
677	2	0.0055068
678	1	0.7702270
678	2	0.2297730
679	1	0.0014450
679	2	0.9985550
680	1	0.0013872
680	2	0.9986128
681	1	0.4036221
681	2	0.5963779
682	1	0.0006885
682	2	0.9993115
683	1	0.0007403
683	2	0.9992597
684	1	0.9978627
684	2	0.0021373
685	1	0.0007436
685	2	0.9992564
686	1	0.5107720
686	2	0.4892280
687	1	0.3715023
687	2	0.6284977
688	1	0.6385309
688	2	0.3614691
689	1	0.4640184
689	2	0.5359816
690	1	0.0009040
690	2	0.9990960
691	1	0.0215330
691	2	0.9784670
692	1	0.9984053
692	2	0.0015947
693	1	0.0028639
693	2	0.9971361
694	1	0.7580681
694	2	0.2419319
695	1	0.9708232
695	2	0.0291768
696	1	0.0018441
696	2	0.9981559
697	1	0.9992733
697	2	0.0007267
698	1	0.2857163
698	2	0.7142837
699	1	0.9772887
699	2	0.0227113
700	1	0.2461009
700	2	0.7538991
701	1	0.8722144
701	2	0.1277856
702	1	0.6603354
702	2	0.3396646
703	1	0.9979917
703	2	0.0020083
704	1	0.0008414
704	2	0.9991586
705	1	0.7752241
705	2	0.2247759
706	1	0.3705825
706	2	0.6294175
707	1	0.4617397
707	2	0.5382603
708	1	0.9989453
708	2	0.0010547
709	1	0.9020898
709	2	0.0979102
710	1	0.0029807
710	2	0.9970193
711	1	0.0012891
711	2	0.9987109
712	1	0.5189568
712	2	0.4810432
713	1	0.3013550
713	2	0.6986450
714	1	0.9957428
714	2	0.0042572
715	1	0.4188455
715	2	0.5811545
716	1	0.9984684
716	2	0.0015316
717	1	0.9970718
717	2	0.0029282
718	1	0.2415966
718	2	0.7584034
719	1	0.0227825
719	2	0.9772175
720	1	0.9930960
720	2	0.0069040
721	1	0.0006656
721	2	0.9993344
722	1	0.5921166
722	2	0.4078834
723	1	0.0328525
723	2	0.9671475
724	1	0.0020186
724	2	0.9979814
725	1	0.3972351
725	2	0.6027649
726	1	0.9991776
726	2	0.0008224
727	1	0.9990896
727	2	0.0009104
728	1	0.1670188
728	2	0.8329812
729	1	0.0238832
729	2	0.9761168
730	1	0.9977475
730	2	0.0022525
731	1	0.3798773
731	2	0.6201227
732	1	0.9984814
732	2	0.0015186
733	1	0.0006655
733	2	0.9993345
734	1	0.8741108
734	2	0.1258892
735	1	0.0031383
735	2	0.9968617
736	1	0.5538190
736	2	0.4461810
737	1	0.0404595
737	2	0.9595405
738	1	0.5158122
738	2	0.4841878
739	1	0.9437798
739	2	0.0562202
740	1	0.9987239
740	2	0.0012761
741	1	0.9989413
741	2	0.0010587
742	1	0.8337616
742	2	0.1662384
743	1	0.0014167
743	2	0.9985833
744	1	0.2882217
744	2	0.7117783
745	1	0.0016937
745	2	0.9983063
746	1	0.9990877
746	2	0.0009123
747	1	0.0012824
747	2	0.9987176
748	1	0.3326425
748	2	0.6673575
749	1	0.0047735
749	2	0.9952265
750	1	0.7994443
750	2	0.2005557
751	1	0.1571127
751	2	0.8428873
752	1	0.4223029
752	2	0.5776971
753	1	0.0016251
753	2	0.9983749
754	1	0.0326231
754	2	0.9673769
755	1	0.3994596
755	2	0.6005404
756	1	0.9982740
756	2	0.0017260
757	1	0.9620525
757	2	0.0379475
758	1	0.9985173
758	2	0.0014827
759	1	0.0005796
759	2	0.9994204
760	1	0.3977101
760	2	0.6022899
761	1	0.3171261
761	2	0.6828739
762	1	0.9979761
762	2	0.0020239
763	1	0.5331796
763	2	0.4668204
764	1	0.2202087
764	2	0.7797913
765	1	0.9963974
765	2	0.0036026
766	1	0.3527613
766	2	0.6472387
767	1	0.0016614
767	2	0.9983386
768	1	0.9668668
768	2	0.0331332
769	1	0.9561704
769	2	0.0438296
770	1	0.2588163
770	2	0.7411837
771	1	0.2157257
771	2	0.7842743
772	1	0.0320206
772	2	0.9679794
773	1	0.8621505
773	2	0.1378495
774	1	0.0013492
774	2	0.9986508
775	1	0.9710017
775	2	0.0289983
776	1	0.1889791
776	2	0.8110209
777	1	0.0476473
777	2	0.9523527
778	1	0.8679275
778	2	0.1320725
779	1	0.5594779
779	2	0.4405221
780	1	0.9989186
780	2	0.0010814
781	1	0.0255510
781	2	0.9744490
782	1	0.8695328
782	2	0.1304672
783	1	0.0007570
783	2	0.9992430
784	1	0.0473750
784	2	0.9526250
785	1	0.8036457
785	2	0.1963543
786	1	0.3298628
786	2	0.6701372
787	1	0.0009227
787	2	0.9990773
788	1	0.0015353
788	2	0.9984647
789	1	0.6537210
789	2	0.3462790
790	1	0.0007280
790	2	0.9992720
791	1	0.9957023
791	2	0.0042977
792	1	0.8498869
792	2	0.1501131
793	1	0.9984387
793	2	0.0015613
794	1	0.0040477
794	2	0.9959523
795	1	0.0008952
795	2	0.9991048
796	1	0.4980644
796	2	0.5019356
797	1	0.1944856
797	2	0.8055144
798	1	0.2182168
798	2	0.7817832
799	1	0.0007311
799	2	0.9992689
800	1	0.8214927
800	2	0.1785073
801	1	0.3122365
801	2	0.6877635
802	1	0.0008968
802	2	0.9991032
803	1	0.0014205
803	2	0.9985795
804	1	0.0018212
804	2	0.9981788
805	1	0.7127718
805	2	0.2872282
806	1	0.3736568
806	2	0.6263432
807	1	0.9983816
807	2	0.0016184
808	1	0.0681190
808	2	0.9318810
809	1	0.0141503
809	2	0.9858497
810	1	0.8683444
810	2	0.1316556
811	1	0.1865082
811	2	0.8134918
812	1	0.5918865
812	2	0.4081135
813	1	0.9989166
813	2	0.0010834
814	1	0.9990863
814	2	0.0009137
815	1	0.0016869
815	2	0.9983131
816	1	0.4595304
816	2	0.5404696
817	1	0.6498059
817	2	0.3501941
818	1	0.3021328
818	2	0.6978672
819	1	0.1704291
819	2	0.8295709
820	1	0.0019860
820	2	0.9980140
821	1	0.9974332
821	2	0.0025668
822	1	0.5356131
822	2	0.4643869
823	1	0.0010685
823	2	0.9989315
824	1	0.8758233
824	2	0.1241767
825	1	0.2157146
825	2	0.7842854
826	1	0.2466894
826	2	0.7533106
827	1	0.4319415
827	2	0.5680585
828	1	0.1333303
828	2	0.8666697
829	1	0.4129034
829	2	0.5870966
830	1	0.2584864
830	2	0.7415136
831	1	0.6261386
831	2	0.3738614
832	1	0.0014813
832	2	0.9985187
833	1	0.0007207
833	2	0.9992793
834	1	0.9980856
834	2	0.0019144
835	1	0.0816313
835	2	0.9183687
836	1	0.6370707
836	2	0.3629293
837	1	0.9959976
837	2	0.0040024
838	1	0.0005481
838	2	0.9994519
839	1	0.4838243
839	2	0.5161757
840	1	0.3013747
840	2	0.6986253
841	1	0.9985594
841	2	0.0014406
842	1	0.6608211
842	2	0.3391789
843	1	0.0008945
843	2	0.9991055
844	1	0.1075712
844	2	0.8924288
845	1	0.9766190
845	2	0.0233810
846	1	0.9958785
846	2	0.0041215
847	1	0.0930079
847	2	0.9069921
848	1	0.0006697
848	2	0.9993303
849	1	0.9985682
849	2	0.0014318
850	1	0.6179496
850	2	0.3820504
851	1	0.0343950
851	2	0.9656050
852	1	0.0022754
852	2	0.9977246
853	1	0.9990323
853	2	0.0009677
854	1	0.1383763
854	2	0.8616237
855	1	0.7198008
855	2	0.2801992
856	1	0.0934658
856	2	0.9065342
857	1	0.6506188
857	2	0.3493812
858	1	0.9991349
858	2	0.0008651
859	1	0.1637842
859	2	0.8362158
860	1	0.2239380
860	2	0.7760620
861	1	0.2779703
861	2	0.7220297
862	1	0.3959432
862	2	0.6040568
863	1	0.0009176
863	2	0.9990824
864	1	0.2490658
864	2	0.7509342
865	1	0.1535796
865	2	0.8464204
866	1	0.0005676
866	2	0.9994324
867	1	0.2570428
867	2	0.7429572
868	1	0.9964298
868	2	0.0035702
869	1	0.9990840
869	2	0.0009160
870	1	0.0016792
870	2	0.9983208
871	1	0.9987736
871	2	0.0012264
872	1	0.3101762
872	2	0.6898238
873	1	0.6325897
873	2	0.3674103
874	1	0.5478141
874	2	0.4521859
875	1	0.0007655
875	2	0.9992345
876	1	0.9063689
876	2	0.0936311
877	1	0.1551901
877	2	0.8448099
878	1	0.2493850
878	2	0.7506150
879	1	0.6621116
879	2	0.3378884
880	1	0.9973067
880	2	0.0026933
881	1	0.0021627
881	2	0.9978373
882	1	0.8716056
882	2	0.1283944
883	1	0.9985937
883	2	0.0014063
884	1	0.0010606
884	2	0.9989394
885	1	0.4869206
885	2	0.5130794
886	1	0.8714246
886	2	0.1285754
887	1	0.4157939
887	2	0.5842061
888	1	0.0272376
888	2	0.9727624
889	1	0.5715446
889	2	0.4284554
890	1	0.0010149
890	2	0.9989851
891	1	0.0005833
891	2	0.9994167
892	1	0.7546728
892	2	0.2453272
893	1	0.0012714
893	2	0.9987286
894	1	0.9954862
894	2	0.0045138
895	1	0.4627628
895	2	0.5372372
896	1	0.7293012
896	2	0.2706988
897	1	0.9984936
897	2	0.0015064
898	1	0.0873719
898	2	0.9126281
899	1	0.4186667
899	2	0.5813333
900	1	0.0013449
900	2	0.9986551
901	1	0.0035254
901	2	0.9964746
902	1	0.4147095
902	2	0.5852905
903	1	0.5102444
903	2	0.4897556
904	1	0.2247212
904	2	0.7752788
905	1	0.0036766
905	2	0.9963234
906	1	0.9985223
906	2	0.0014777
907	1	0.4812335
907	2	0.5187665
908	1	0.9980644
908	2	0.0019356
909	1	0.5264576
909	2	0.4735424
910	1	0.0610428
910	2	0.9389572
911	1	0.2524382
911	2	0.7475618
912	1	0.9990132
912	2	0.0009868
913	1	0.0009808
913	2	0.9990192
914	1	0.1767561
914	2	0.8232439
915	1	0.8133025
915	2	0.1866975
916	1	0.0041605
916	2	0.9958395
917	1	0.0942520
917	2	0.9057480
918	1	0.2031837
918	2	0.7968163
919	1	0.0009438
919	2	0.9990562
920	1	0.9977642
920	2	0.0022358
921	1	0.9985242
921	2	0.0014758
922	1	0.9990995
922	2	0.0009005
923	1	0.5564053
923	2	0.4435947
924	1	0.4332767
924	2	0.5667233
925	1	0.9773016
925	2	0.0226984
926	1	0.3707389
926	2	0.6292611
927	1	0.5602932
927	2	0.4397068
928	1	0.0374755
928	2	0.9625245
929	1	0.0006887
929	2	0.9993113
930	1	0.4081429
930	2	0.5918571
931	1	0.9990887
931	2	0.0009113
932	1	0.4152805
932	2	0.5847195
933	1	0.9989616
933	2	0.0010384
934	1	0.1596166
934	2	0.8403834
935	1	0.0028372
935	2	0.9971628
936	1	0.0637024
936	2	0.9362976
937	1	0.0541144
937	2	0.9458856
938	1	0.5466898
938	2	0.4533102
939	1	0.0018102
939	2	0.9981898
940	1	0.1036493
940	2	0.8963507
941	1	0.7341277
941	2	0.2658723
942	1	0.0924390
942	2	0.9075610
943	1	0.0006699
943	2	0.9993301
944	1	0.9988026
944	2	0.0011974
945	1	0.1645389
945	2	0.8354611
946	1	0.2358798
946	2	0.7641202
947	1	0.0009989
947	2	0.9990011
948	1	0.3059328
948	2	0.6940672
949	1	0.6946357
949	2	0.3053643
950	1	0.1516597
950	2	0.8483403
951	1	0.3764269
951	2	0.6235731
952	1	0.0036404
952	2	0.9963596
953	1	0.2939892
953	2	0.7060108
954	1	0.8318773
954	2	0.1681227
955	1	0.9989421
955	2	0.0010579
956	1	0.1042751
956	2	0.8957249
957	1	0.0043678
957	2	0.9956322
958	1	0.1852779
958	2	0.8147221
959	1	0.1063142
959	2	0.8936858
960	1	0.0368951
960	2	0.9631049
961	1	0.3208584
961	2	0.6791416
962	1	0.9979794
962	2	0.0020206
963	1	0.1694488
963	2	0.8305512
964	1	0.6917997
964	2	0.3082003
965	1	0.9678955
965	2	0.0321045
966	1	0.0012838
966	2	0.9987162
967	1	0.3804537
967	2	0.6195463
968	1	0.9985130
968	2	0.0014870
969	1	0.0013181
969	2	0.9986819
970	1	0.1511584
970	2	0.8488416
971	1	0.0007045
971	2	0.9992955
972	1	0.0033448
972	2	0.9966552
973	1	0.6644224
973	2	0.3355776
974	1	0.6210734
974	2	0.3789266
975	1	0.9985734
975	2	0.0014266
976	1	0.9950140
976	2	0.0049860
977	1	0.0015323
977	2	0.9984677
978	1	0.0124852
978	2	0.9875148
979	1	0.1480107
979	2	0.8519893
980	1	0.0008341
980	2	0.9991659
981	1	0.2741165
981	2	0.7258835
982	1	0.1430367
982	2	0.8569633
983	1	0.7497756
983	2	0.2502244
984	1	0.2112012
984	2	0.7887988
985	1	0.0017072
985	2	0.9982928
986	1	0.6117941
986	2	0.3882059
987	1	0.0009877
987	2	0.9990123
988	1	0.9924486
988	2	0.0075514
989	1	0.0021996
989	2	0.9978004
990	1	0.5111589
990	2	0.4888411
991	1	0.1417600
991	2	0.8582400
992	1	0.6871701
992	2	0.3128299
993	1	0.0018077
993	2	0.9981923
994	1	0.0026446
994	2	0.9973554
995	1	0.6246286
995	2	0.3753714
996	1	0.7665855
996	2	0.2334145
997	1	0.0283847
997	2	0.9716153
998	1	0.1073722
998	2	0.8926278
999	1	0.0049441
999	2	0.9950559
1000	1	0.5537170
1000	2	0.4462830
1001	1	0.9855246
1001	2	0.0144754
1002	1	0.2482876
1002	2	0.7517124
1003	1	0.0242204
1003	2	0.9757796
1004	1	0.0023576
1004	2	0.9976424
1005	1	0.5407164
1005	2	0.4592836
1006	1	0.1444545
1006	2	0.8555455
1007	1	0.0408341
1007	2	0.9591659
1008	1	0.9973340
1008	2	0.0026660
1009	1	0.8542056
1009	2	0.1457944
1010	1	0.0028121
1010	2	0.9971879
1011	1	0.9985621
1011	2	0.0014379
1012	1	0.2789988
1012	2	0.7210012
1013	1	0.7791737
1013	2	0.2208263
1014	1	0.8936871
1014	2	0.1063129
1015	1	0.4535659
1015	2	0.5464341
1016	1	0.4372204
1016	2	0.5627796
1017	1	0.6296963
1017	2	0.3703037
1018	1	0.1320389
1018	2	0.8679611
1019	1	0.5106969
1019	2	0.4893031
1020	1	0.9673386
1020	2	0.0326614
1021	1	0.9986201
1021	2	0.0013799
1022	1	0.8936470
1022	2	0.1063530
1023	1	0.3622730
1023	2	0.6377270
1024	1	0.0015566
1024	2	0.9984434
1025	1	0.6251896
1025	2	0.3748104
1026	1	0.0008777
1026	2	0.9991223
1027	1	0.2694857
1027	2	0.7305143
1028	1	0.9955817
1028	2	0.0044183
1029	1	0.6559663
1029	2	0.3440337
1030	1	0.2614618
1030	2	0.7385382
1031	1	0.9946545
1031	2	0.0053455
1032	1	0.5415852
1032	2	0.4584148
1033	1	0.1055177
1033	2	0.8944823
1034	1	0.4628520
1034	2	0.5371480
1035	1	0.2814167
1035	2	0.7185833
1036	1	0.0857329
1036	2	0.9142671
1037	1	0.1029079
1037	2	0.8970921
1038	1	0.1405853
1038	2	0.8594147
1039	1	0.6089283
1039	2	0.3910717
1040	1	0.4673958
1040	2	0.5326042
1041	1	0.9989305
1041	2	0.0010695
1042	1	0.9950693
1042	2	0.0049307
1043	1	0.0814680
1043	2	0.9185320
1044	1	0.9988703
1044	2	0.0011297
1045	1	0.9105910
1045	2	0.0894090
1046	1	0.4211698
1046	2	0.5788302
1047	1	0.9983802
1047	2	0.0016198
1048	1	0.9989310
1048	2	0.0010690
1049	1	0.9970336
1049	2	0.0029664
1050	1	0.7675886
1050	2	0.2324114
1051	1	0.0011336
1051	2	0.9988664
1052	1	0.9984113
1052	2	0.0015887
1053	1	0.9993105
1053	2	0.0006895
1054	1	0.9977539
1054	2	0.0022461
1055	1	0.4303856
1055	2	0.5696144
1056	1	0.6050553
1056	2	0.3949447
1057	1	0.1800880
1057	2	0.8199120
1058	1	0.0009152
1058	2	0.9990848
1059	1	0.4204347
1059	2	0.5795653
1060	1	0.9993073
1060	2	0.0006927
1061	1	0.5308759
1061	2	0.4691241
1062	1	0.6947477
1062	2	0.3052523
1063	1	0.9980744
1063	2	0.0019256
1064	1	0.9992897
1064	2	0.0007103
1065	1	0.1650710
1065	2	0.8349290
1066	1	0.9989073
1066	2	0.0010927
1067	1	0.0013700
1067	2	0.9986300
1068	1	0.0369416
1068	2	0.9630584
1069	1	0.0234446
1069	2	0.9765554
1070	1	0.0005655
1070	2	0.9994345
1071	1	0.4590764
1071	2	0.5409236
1072	1	0.4150344
1072	2	0.5849656
1073	1	0.9981345
1073	2	0.0018655
1074	1	0.0318341
1074	2	0.9681659
1075	1	0.2621024
1075	2	0.7378976
1076	1	0.0179831
1076	2	0.9820169
1077	1	0.9956638
1077	2	0.0043362
1078	1	0.9980476
1078	2	0.0019524
1079	1	0.0012123
1079	2	0.9987877
1080	1	0.5734365
1080	2	0.4265635
1081	1	0.3109521
1081	2	0.6890479
1082	1	0.9982447
1082	2	0.0017553
1083	1	0.3604141
1083	2	0.6395859
1084	1	0.0429354
1084	2	0.9570646
1085	1	0.6627522
1085	2	0.3372478
1086	1	0.5030411
1086	2	0.4969589
1087	1	0.9857272
1087	2	0.0142728
1088	1	0.0561363
1088	2	0.9438637
1089	1	0.6389780
1089	2	0.3610220
1090	1	0.9973701
1090	2	0.0026299
1091	1	0.9986341
1091	2	0.0013659
1092	1	0.3273296
1092	2	0.6726704
1093	1	0.9990345
1093	2	0.0009655
1094	1	0.5751332
1094	2	0.4248668
1095	1	0.7726874
1095	2	0.2273126
1096	1	0.0020938
1096	2	0.9979062
1097	1	0.0509158
1097	2	0.9490842
1098	1	0.0289778
1098	2	0.9710222
1099	1	0.0321733
1099	2	0.9678267
1100	1	0.5114356
1100	2	0.4885644
1101	1	0.1597891
1101	2	0.8402109
1102	1	0.5314355
1102	2	0.4685645
1103	1	0.0014874
1103	2	0.9985126
1104	1	0.0561572
1104	2	0.9438428
1105	1	0.7804156
1105	2	0.2195844
1106	1	0.1051491
1106	2	0.8948509
1107	1	0.0378765
1107	2	0.9621235
1108	1	0.9983478
1108	2	0.0016522
1109	1	0.9004705
1109	2	0.0995295
1110	1	0.3032596
1110	2	0.6967404
1111	1	0.9993306
1111	2	0.0006694
1112	1	0.9966771
1112	2	0.0033229
1113	1	0.9991032
1113	2	0.0008968
1114	1	0.6778137
1114	2	0.3221863
1115	1	0.3471148
1115	2	0.6528852
1116	1	0.9493978
1116	2	0.0506022
1117	1	0.0011648
1117	2	0.9988352
1118	1	0.3166028
1118	2	0.6833972
1119	1	0.0010239
1119	2	0.9989761
1120	1	0.5609651
1120	2	0.4390349
1121	1	0.9704257
1121	2	0.0295743
1122	1	0.9992211
1122	2	0.0007789
1123	1	0.0027168
1123	2	0.9972832
1124	1	0.1576308
1124	2	0.8423692
1125	1	0.0014184
1125	2	0.9985816
1126	1	0.0457608
1126	2	0.9542392
1127	1	0.2486226
1127	2	0.7513774
1128	1	0.1353530
1128	2	0.8646470
1129	1	0.0234762
1129	2	0.9765238
1130	1	0.0009315
1130	2	0.9990685
1131	1	0.9978041
1131	2	0.0021959
1132	1	0.0006311
1132	2	0.9993689
1133	1	0.0213580
1133	2	0.9786420
1134	1	0.5865716
1134	2	0.4134284
1135	1	0.2221516
1135	2	0.7778484
1136	1	0.0185572
1136	2	0.9814428
1137	1	0.0039000
1137	2	0.9961000
1138	1	0.3457848
1138	2	0.6542152
1139	1	0.0008008
1139	2	0.9991992
1140	1	0.0012075
1140	2	0.9987925
1141	1	0.3566412
1141	2	0.6433588
1142	1	0.2697530
1142	2	0.7302470
1143	1	0.9159914
1143	2	0.0840086
1144	1	0.9624992
1144	2	0.0375008
1145	1	0.0532782
1145	2	0.9467218
1146	1	0.0521539
1146	2	0.9478461
1147	1	0.2031734
1147	2	0.7968266
1148	1	0.0975568
1148	2	0.9024432
1149	1	0.8349526
1149	2	0.1650474
1150	1	0.0013944
1150	2	0.9986056
1151	1	0.9845092
1151	2	0.0154908
1152	1	0.9967159
1152	2	0.0032841
1153	1	0.9985365
1153	2	0.0014635
1154	1	0.6826007
1154	2	0.3173993
1155	1	0.2949806
1155	2	0.7050194
1156	1	0.0822867
1156	2	0.9177133
1157	1	0.9983659
1157	2	0.0016341
1158	1	0.1407611
1158	2	0.8592389
1159	1	0.2356795
1159	2	0.7643205
1160	1	0.0027440
1160	2	0.9972560
1161	1	0.3909010
1161	2	0.6090990
1162	1	0.2413634
1162	2	0.7586366
1163	1	0.9963921
1163	2	0.0036079
1164	1	0.3411016
1164	2	0.6588984
1165	1	0.9983940
1165	2	0.0016060
1166	1	0.0032091
1166	2	0.9967909
1167	1	0.0010556
1167	2	0.9989444
1168	1	0.7486272
1168	2	0.2513728
1169	1	0.6863792
1169	2	0.3136208
1170	1	0.3149691
1170	2	0.6850309
1171	1	0.3480285
1171	2	0.6519715
1172	1	0.2739524
1172	2	0.7260476
1173	1	0.3787619
1173	2	0.6212381
1174	1	0.2275724
1174	2	0.7724276
1175	1	0.9978723
1175	2	0.0021277
1176	1	0.0683270
1176	2	0.9316730
1177	1	0.0007068
1177	2	0.9992932
1178	1	0.0014882
1178	2	0.9985118
1179	1	0.2884027
1179	2	0.7115973
1180	1	0.1306553
1180	2	0.8693447
1181	1	0.0014390
1181	2	0.9985610
1182	1	0.4858188
1182	2	0.5141812
1183	1	0.9955818
1183	2	0.0044182
1184	1	0.5360708
1184	2	0.4639292
1185	1	0.4718284
1185	2	0.5281716
1186	1	0.0066085
1186	2	0.9933915
1187	1	0.0009094
1187	2	0.9990906
1188	1	0.5296410
1188	2	0.4703590
1189	1	0.9993512
1189	2	0.0006488
1190	1	0.9979684
1190	2	0.0020316
1191	1	0.0314549
1191	2	0.9685451
1192	1	0.4938091
1192	2	0.5061909
1193	1	0.3481590
1193	2	0.6518410
1194	1	0.9510084
1194	2	0.0489916
1195	1	0.0453118
1195	2	0.9546882
1196	1	0.1149821
1196	2	0.8850179
1197	1	0.9991722
1197	2	0.0008278
1198	1	0.0014961
1198	2	0.9985039
1199	1	0.9981328
1199	2	0.0018672
1200	1	0.1343941
1200	2	0.8656059
1201	1	0.0024000
1201	2	0.9976000
1202	1	0.9971172
1202	2	0.0028828
1203	1	0.2135331
1203	2	0.7864669
1204	1	0.4885186
1204	2	0.5114814
1205	1	0.0015935
1205	2	0.9984065
1206	1	0.2988283
1206	2	0.7011717
1207	1	0.4041036
1207	2	0.5958964
1208	1	0.9767369
1208	2	0.0232631
1209	1	0.2085734
1209	2	0.7914266
1210	1	0.0151835
1210	2	0.9848165
1211	1	0.9397151
1211	2	0.0602849
1212	1	0.4479511
1212	2	0.5520489
1213	1	0.2611325
1213	2	0.7388675
1214	1	0.7022517
1214	2	0.2977483
1215	1	0.9962701
1215	2	0.0037299
1216	1	0.1710551
1216	2	0.8289449
1217	1	0.0748142
1217	2	0.9251858
1218	1	0.6476882
1218	2	0.3523118
1219	1	0.6364733
1219	2	0.3635267
1220	1	0.4327231
1220	2	0.5672769
1221	1	0.9989620
1221	2	0.0010380
1222	1	0.1739957
1222	2	0.8260043
1223	1	0.0011936
1223	2	0.9988064
1224	1	0.3466980
1224	2	0.6533020
1225	1	0.7473864
1225	2	0.2526136
1226	1	0.5097634
1226	2	0.4902366
1227	1	0.9979457
1227	2	0.0020543
1228	1	0.4626272
1228	2	0.5373728
1229	1	0.4987195
1229	2	0.5012805
1230	1	0.2714555
1230	2	0.7285445
1231	1	0.2197683
1231	2	0.7802317
1232	1	0.9987075
1232	2	0.0012925
1233	1	0.3309147
1233	2	0.6690853
1234	1	0.0006841
1234	2	0.9993159
1235	1	0.3456202
1235	2	0.6543798
1236	1	0.0010280
1236	2	0.9989720
1237	1	0.0087291
1237	2	0.9912709
1238	1	0.7939921
1238	2	0.2060079
1239	1	0.0016246
1239	2	0.9983754
1240	1	0.2094736
1240	2	0.7905264
1241	1	0.0011889
1241	2	0.9988111
1242	1	0.6369275
1242	2	0.3630725
1243	1	0.0747437
1243	2	0.9252563
1244	1	0.2045269
1244	2	0.7954731
1245	1	0.2846251
1245	2	0.7153749
1246	1	0.3237720
1246	2	0.6762280
1247	1	0.6819437
1247	2	0.3180563
1248	1	0.2448506
1248	2	0.7551494
1249	1	0.9981112
1249	2	0.0018888
1250	1	0.6713175
1250	2	0.3286825
1251	1	0.0707603
1251	2	0.9292397
1252	1	0.9942260
1252	2	0.0057740
1253	1	0.0035732
1253	2	0.9964268
1254	1	0.3208560
1254	2	0.6791440
1255	1	0.9967545
1255	2	0.0032455
1256	1	0.0020733
1256	2	0.9979267
1257	1	0.3049248
1257	2	0.6950752
1258	1	0.5092305
1258	2	0.4907695
1259	1	0.0012307
1259	2	0.9987693
1260	1	0.3105197
1260	2	0.6894803
1261	1	0.7868424
1261	2	0.2131576
1262	1	0.2732722
1262	2	0.7267278
1263	1	0.5852475
1263	2	0.4147525
1264	1	0.5183838
1264	2	0.4816162
1265	1	0.3119515
1265	2	0.6880485
1266	1	0.0370947
1266	2	0.9629053
1267	1	0.0023259
1267	2	0.9976741
1268	1	0.0484972
1268	2	0.9515028
1269	1	0.6212340
1269	2	0.3787660
1270	1	0.1059652
1270	2	0.8940348
1271	1	0.9984931
1271	2	0.0015069
1272	1	0.0019921
1272	2	0.9980079
1273	1	0.0650392
1273	2	0.9349608
1274	1	0.0016774
1274	2	0.9983226
1275	1	0.0391137
1275	2	0.9608863
1276	1	0.0007145
1276	2	0.9992855
1277	1	0.0280421
1277	2	0.9719579
1278	1	0.1431503
1278	2	0.8568497
1279	1	0.0013145
1279	2	0.9986855
1280	1	0.8362842
1280	2	0.1637158
1281	1	0.0026217
1281	2	0.9973783
1282	1	0.0746177
1282	2	0.9253823
1283	1	0.1380783
1283	2	0.8619217
1284	1	0.9930598
1284	2	0.0069402
1285	1	0.9974310
1285	2	0.0025690
1286	1	0.3620303
1286	2	0.6379697
1287	1	0.6123590
1287	2	0.3876410
1288	1	0.0009791
1288	2	0.9990209
1289	1	0.6037852
1289	2	0.3962148
1290	1	0.0024430
1290	2	0.9975570
1291	1	0.2149020
1291	2	0.7850980
1292	1	0.1761218
1292	2	0.8238782
1293	1	0.1457136
1293	2	0.8542864
1294	1	0.0019335
1294	2	0.9980665
1295	1	0.9978470
1295	2	0.0021530
1296	1	0.9973091
1296	2	0.0026909
1297	1	0.1839812
1297	2	0.8160188
1298	1	0.4446760
1298	2	0.5553240
1299	1	0.9986637
1299	2	0.0013363
1300	1	0.1985258
1300	2	0.8014742
1301	1	0.0823210
1301	2	0.9176790
1302	1	0.0023090
1302	2	0.9976910
1303	1	0.0018028
1303	2	0.9981972
1304	1	0.2401283
1304	2	0.7598717
1305	1	0.7413017
1305	2	0.2586983
1306	1	0.2129060
1306	2	0.7870940
1307	1	0.0237181
1307	2	0.9762819
1308	1	0.0015683
1308	2	0.9984317
1309	1	0.9987927
1309	2	0.0012073
1310	1	0.1775625
1310	2	0.8224375
1311	1	0.0904229
1311	2	0.9095771
1312	1	0.0044915
1312	2	0.9955085
1313	1	0.0010754
1313	2	0.9989246
1314	1	0.4712358
1314	2	0.5287642
1315	1	0.6845681
1315	2	0.3154319
1316	1	0.0662639
1316	2	0.9337361
1317	1	0.0012452
1317	2	0.9987548
1318	1	0.4173841
1318	2	0.5826159
1319	1	0.0293172
1319	2	0.9706828
1320	1	0.5391828
1320	2	0.4608172
1321	1	0.6180073
1321	2	0.3819927
1322	1	0.9989526
1322	2	0.0010474
1323	1	0.2875889
1323	2	0.7124111
1324	1	0.2894158
1324	2	0.7105842
1325	1	0.0379518
1325	2	0.9620482
1326	1	0.3651429
1326	2	0.6348571
1327	1	0.0115789
1327	2	0.9884211
1328	1	0.2927139
1328	2	0.7072861
1329	1	0.9985868
1329	2	0.0014132
1330	1	0.9992343
1330	2	0.0007657
1331	1	0.4760009
1331	2	0.5239991
1332	1	0.0020553
1332	2	0.9979447
1333	1	0.7448388
1333	2	0.2551612
1334	1	0.9333315
1334	2	0.0666685
1335	1	0.3571763
1335	2	0.6428237
1336	1	0.4744635
1336	2	0.5255365
1337	1	0.0009518
1337	2	0.9990482
1338	1	0.6492896
1338	2	0.3507104
1339	1	0.0177810
1339	2	0.9822190
1340	1	0.7927548
1340	2	0.2072452
1341	1	0.7951388
1341	2	0.2048612
1342	1	0.7380613
1342	2	0.2619387
1343	1	0.0013624
1343	2	0.9986376
1344	1	0.2694880
1344	2	0.7305120
1345	1	0.0033040
1345	2	0.9966960
1346	1	0.6292461
1346	2	0.3707539
1347	1	0.0560993
1347	2	0.9439007
1348	1	0.4275781
1348	2	0.5724219
1349	1	0.9979512
1349	2	0.0020488
1350	1	0.9976234
1350	2	0.0023766
1351	1	0.4661352
1351	2	0.5338648
1352	1	0.6855893
1352	2	0.3144107
1353	1	0.0023120
1353	2	0.9976880
1354	1	0.9822097
1354	2	0.0177903
1355	1	0.4798090
1355	2	0.5201910
1356	1	0.0006327
1356	2	0.9993673
1357	1	0.9977972
1357	2	0.0022028
1358	1	0.4097621
1358	2	0.5902379
1359	1	0.0633899
1359	2	0.9366101
1360	1	0.2965320
1360	2	0.7034680
1361	1	0.9993010
1361	2	0.0006990
1362	1	0.2818908
1362	2	0.7181092
1363	1	0.9985350
1363	2	0.0014650
1364	1	0.0425782
1364	2	0.9574218
1365	1	0.7884679
1365	2	0.2115321
1366	1	0.1308494
1366	2	0.8691506
1367	1	0.0427902
1367	2	0.9572098
1368	1	0.7271423
1368	2	0.2728577
1369	1	0.3109980
1369	2	0.6890020
1370	1	0.4526178
1370	2	0.5473822
1371	1	0.0007925
1371	2	0.9992075
1372	1	0.9968189
1372	2	0.0031811
1373	1	0.3812140
1373	2	0.6187860
1374	1	0.9622661
1374	2	0.0377339
1375	1	0.4070805
1375	2	0.5929195
1376	1	0.9963619
1376	2	0.0036381
1377	1	0.0013667
1377	2	0.9986333
1378	1	0.0013700
1378	2	0.9986300
1379	1	0.9984139
1379	2	0.0015861
1380	1	0.0007390
1380	2	0.9992610
1381	1	0.0013319
1381	2	0.9986681
1382	1	0.9634589
1382	2	0.0365411
1383	1	0.5144573
1383	2	0.4855427
1384	1	0.1681433
1384	2	0.8318567
1385	1	0.0007525
1385	2	0.9992475
1386	1	0.9554766
1386	2	0.0445234
1387	1	0.5099123
1387	2	0.4900877
1388	1	0.0009650
1388	2	0.9990350
1389	1	0.6579981
1389	2	0.3420019
1390	1	0.4794205
1390	2	0.5205795
1391	1	0.3213781
1391	2	0.6786219
1392	1	0.0012563
1392	2	0.9987437
1393	1	0.0028088
1393	2	0.9971912
1394	1	0.0008456
1394	2	0.9991544
1395	1	0.0010890
1395	2	0.9989110
1396	1	0.0008468
1396	2	0.9991532
1397	1	0.5597851
1397	2	0.4402149
1398	1	0.0015627
1398	2	0.9984373
1399	1	0.0010179
1399	2	0.9989821
1400	1	0.0320770
1400	2	0.9679230
1401	1	0.4983871
1401	2	0.5016129
1402	1	0.6838698
1402	2	0.3161302
1403	1	0.4055089
1403	2	0.5944911
1404	1	0.5490310
1404	2	0.4509690
1405	1	0.4775883
1405	2	0.5224117
1406	1	0.1036493
1406	2	0.8963507
1407	1	0.4849885
1407	2	0.5150115
1408	1	0.0361562
1408	2	0.9638438
1409	1	0.8826597
1409	2	0.1173403
1410	1	0.1122326
1410	2	0.8877674
1411	1	0.5748899
1411	2	0.4251101
1412	1	0.9978515
1412	2	0.0021485
1413	1	0.9971943
1413	2	0.0028057
1414	1	0.2543737
1414	2	0.7456263
1415	1	0.5772356
1415	2	0.4227644
1416	1	0.9981841
1416	2	0.0018159
1417	1	0.0861946
1417	2	0.9138054
1418	1	0.9978278
1418	2	0.0021722
1419	1	0.6475276
1419	2	0.3524724
1420	1	0.8630621
1420	2	0.1369379
1421	1	0.9993270
1421	2	0.0006730
1422	1	0.9940477
1422	2	0.0059523
1423	1	0.9977869
1423	2	0.0022131
1424	1	0.2617475
1424	2	0.7382525
1425	1	0.1865812
1425	2	0.8134188
1426	1	0.0929493
1426	2	0.9070507
1427	1	0.0010095
1427	2	0.9989905
1428	1	0.9817634
1428	2	0.0182366
1429	1	0.2626640
1429	2	0.7373360
1430	1	0.4238785
1430	2	0.5761215
1431	1	0.0437105
1431	2	0.9562895
1432	1	0.0810314
1432	2	0.9189686
1433	1	0.8388634
1433	2	0.1611366
1434	1	0.4414794
1434	2	0.5585206
1435	1	0.5438362
1435	2	0.4561638
1436	1	0.3921408
1436	2	0.6078592
1437	1	0.9993457
1437	2	0.0006543
1438	1	0.2653393
1438	2	0.7346607
1439	1	0.3729652
1439	2	0.6270348
1440	1	0.0012210
1440	2	0.9987790
1441	1	0.8703669
1441	2	0.1296331
1442	1	0.2678018
1442	2	0.7321982
1443	1	0.9989842
1443	2	0.0010158
1444	1	0.3372135
1444	2	0.6627865
1445	1	0.9494345
1445	2	0.0505655
1446	1	0.0007731
1446	2	0.9992269
1447	1	0.9980500
1447	2	0.0019500
1448	1	0.6680341
1448	2	0.3319659
1449	1	0.6547375
1449	2	0.3452625
1450	1	0.2852741
1450	2	0.7147259
1451	1	0.2734483
1451	2	0.7265517
1452	1	0.9989805
1452	2	0.0010195
1453	1	0.1868540
1453	2	0.8131460
1454	1	0.9392654
1454	2	0.0607346
1455	1	0.1612532
1455	2	0.8387468
1456	1	0.3727601
1456	2	0.6272399
1457	1	0.5256787
1457	2	0.4743213
1458	1	0.1283768
1458	2	0.8716232
1459	1	0.0026670
1459	2	0.9973330
1460	1	0.9563902
1460	2	0.0436098
1461	1	0.0013388
1461	2	0.9986612
1462	1	0.1626172
1462	2	0.8373828
1463	1	0.9990244
1463	2	0.0009756
1464	1	0.6705678
1464	2	0.3294322
1465	1	0.0007073
1465	2	0.9992927
1466	1	0.9983885
1466	2	0.0016115
1467	1	0.4721265
1467	2	0.5278735
1468	1	0.9978145
1468	2	0.0021855
1469	1	0.0013105
1469	2	0.9986895
1470	1	0.3876962
1470	2	0.6123038
1471	1	0.0499842
1471	2	0.9500158
1472	1	0.4972628
1472	2	0.5027372
1473	1	0.0200147
1473	2	0.9799853
1474	1	0.1225293
1474	2	0.8774707
1475	1	0.4964125
1475	2	0.5035875
1476	1	0.9989250
1476	2	0.0010750
1477	1	0.9899469
1477	2	0.0100531
1478	1	0.3327654
1478	2	0.6672346
1479	1	0.2903210
1479	2	0.7096790
1480	1	0.3813057
1480	2	0.6186943
1481	1	0.0005157
1481	2	0.9994843
1482	1	0.9989869
1482	2	0.0010131
1483	1	0.9118955
1483	2	0.0881045
1484	1	0.9981284
1484	2	0.0018716
1485	1	0.4193401
1485	2	0.5806599
1486	1	0.2611726
1486	2	0.7388274
1487	1	0.8204277
1487	2	0.1795723
1488	1	0.9928365
1488	2	0.0071635
1489	1	0.0007851
1489	2	0.9992149
1490	1	0.7193523
1490	2	0.2806477
1491	1	0.9990726
1491	2	0.0009274
1492	1	0.9975895
1492	2	0.0024105
1493	1	0.9961592
1493	2	0.0038408
1494	1	0.0014511
1494	2	0.9985489
1495	1	0.0011165
1495	2	0.9988835
1496	1	0.0013075
1496	2	0.9986925
1497	1	0.9982691
1497	2	0.0017309
1498	1	0.2591469
1498	2	0.7408531
1499	1	0.2797743
1499	2	0.7202257
1500	1	0.8753861
1500	2	0.1246139
1501	1	0.9974373
1501	2	0.0025627
1502	1	0.0006187
1502	2	0.9993813
1503	1	0.0045572
1503	2	0.9954428
1504	1	0.4936358
1504	2	0.5063642
1505	1	0.4451044
1505	2	0.5548956
1506	1	0.7052571
1506	2	0.2947429
1507	1	0.9988822
1507	2	0.0011178
1508	1	0.6532073
1508	2	0.3467927
1509	1	0.9983387
1509	2	0.0016613
1510	1	0.5551143
1510	2	0.4448857
1511	1	0.0018452
1511	2	0.9981548
1512	1	0.2126365
1512	2	0.7873635
1513	1	0.0045814
1513	2	0.9954186
1514	1	0.1057806
1514	2	0.8942194
1515	1	0.6016455
1515	2	0.3983545
1516	1	0.9984660
1516	2	0.0015340
1517	1	0.6935570
1517	2	0.3064430
1518	1	0.0006258
1518	2	0.9993742
1519	1	0.0030026
1519	2	0.9969974
1520	1	0.0067086
1520	2	0.9932914
1521	1	0.8914576
1521	2	0.1085424
1522	1	0.0052657
1522	2	0.9947343
1523	1	0.1179520
1523	2	0.8820480
1524	1	0.9055017
1524	2	0.0944983
1525	1	0.0014183
1525	2	0.9985817
1526	1	0.2228849
1526	2	0.7771151
1527	1	0.3559305
1527	2	0.6440695
1528	1	0.7144906
1528	2	0.2855094
1529	1	0.9984851
1529	2	0.0015149
1530	1	0.7149630
1530	2	0.2850370
1531	1	0.0423109
1531	2	0.9576891
1532	1	0.3541879
1532	2	0.6458121
1533	1	0.0022778
1533	2	0.9977222
1534	1	0.5428628
1534	2	0.4571372
1535	1	0.9988378
1535	2	0.0011622
1536	1	0.5534669
1536	2	0.4465331
1537	1	0.9606656
1537	2	0.0393344
1538	1	0.8765367
1538	2	0.1234633
1539	1	0.7659287
1539	2	0.2340713
1540	1	0.2711236
1540	2	0.7288764
1541	1	0.0024592
1541	2	0.9975408
1542	1	0.9898257
1542	2	0.0101743
1543	1	0.0172034
1543	2	0.9827966
1544	1	0.5694971
1544	2	0.4305029
1545	1	0.7640343
1545	2	0.2359657
1546	1	0.1725645
1546	2	0.8274355
1547	1	0.0844906
1547	2	0.9155094
1548	1	0.1237064
1548	2	0.8762936
1549	1	0.9884154
1549	2	0.0115846
1550	1	0.9933262
1550	2	0.0066738
1551	1	0.0192427
1551	2	0.9807573
1552	1	0.0890068
1552	2	0.9109932
1553	1	0.5923782
1553	2	0.4076218
1554	1	0.7448307
1554	2	0.2551693
1555	1	0.0007624
1555	2	0.9992376
1556	1	0.0010437
1556	2	0.9989563
1557	1	0.0018104
1557	2	0.9981896
1558	1	0.5220021
1558	2	0.4779979
1559	1	0.2995372
1559	2	0.7004628
1560	1	0.5536661
1560	2	0.4463339
1561	1	0.1555190
1561	2	0.8444810
1562	1	0.5461766
1562	2	0.4538234
1563	1	0.7090858
1563	2	0.2909142
1564	1	0.0273219
1564	2	0.9726781
1565	1	0.9836017
1565	2	0.0163983
1566	1	0.2336894
1566	2	0.7663106
1567	1	0.9974131
1567	2	0.0025869
1568	1	0.3214375
1568	2	0.6785625
1569	1	0.8819926
1569	2	0.1180074
1570	1	0.9978355
1570	2	0.0021645
1571	1	0.9102424
1571	2	0.0897576
1572	1	0.1601665
1572	2	0.8398335
1573	1	0.9988773
1573	2	0.0011227
1574	1	0.9953066
1574	2	0.0046934
1575	1	0.9982254
1575	2	0.0017746
1576	1	0.0005518
1576	2	0.9994482
1577	1	0.0291924
1577	2	0.9708076
1578	1	0.0427744
1578	2	0.9572256
1579	1	0.2095178
1579	2	0.7904822
1580	1	0.1804179
1580	2	0.8195821
1581	1	0.2815367
1581	2	0.7184633
1582	1	0.2447654
1582	2	0.7552346
1583	1	0.9978449
1583	2	0.0021551
1584	1	0.9489472
1584	2	0.0510528
1585	1	0.3642910
1585	2	0.6357090
1586	1	0.0008222
1586	2	0.9991778
1587	1	0.9985113
1587	2	0.0014887
1588	1	0.8530357
1588	2	0.1469643
1589	1	0.2240216
1589	2	0.7759784
1590	1	0.3301962
1590	2	0.6698038
1591	1	0.0006382
1591	2	0.9993618
1592	1	0.6063725
1592	2	0.3936275
1593	1	0.9988010
1593	2	0.0011990
1594	1	0.1786801
1594	2	0.8213199
1595	1	0.1675795
1595	2	0.8324205
1596	1	0.2881343
1596	2	0.7118657
1597	1	0.7558353
1597	2	0.2441647
1598	1	0.0010552
1598	2	0.9989448
1599	1	0.2467961
1599	2	0.7532039
1600	1	0.4799014
1600	2	0.5200986
1601	1	0.6593305
1601	2	0.3406695
1602	1	0.0117668
1602	2	0.9882332
1603	1	0.9979257
1603	2	0.0020743
1604	1	0.0139051
1604	2	0.9860949
1605	1	0.4912198
1605	2	0.5087802
1606	1	0.0279931
1606	2	0.9720069
1607	1	0.0010646
1607	2	0.9989354
1608	1	0.9990514
1608	2	0.0009486
1609	1	0.0748777
1609	2	0.9251223
1610	1	0.7366564
1610	2	0.2633436
1611	1	0.1346094
1611	2	0.8653906
1612	1	0.3821883
1612	2	0.6178117
1613	1	0.7526402
1613	2	0.2473598
1614	1	0.0040084
1614	2	0.9959916
1615	1	0.0010812
1615	2	0.9989188
1616	1	0.9268366
1616	2	0.0731634
1617	1	0.5960805
1617	2	0.4039195
1618	1	0.4895953
1618	2	0.5104047
1619	1	0.9984605
1619	2	0.0015395
1620	1	0.2699870
1620	2	0.7300130
1621	1	0.9949998
1621	2	0.0050002
1622	1	0.9942494
1622	2	0.0057506
1623	1	0.7407600
1623	2	0.2592400
1624	1	0.0009985
1624	2	0.9990015
1625	1	0.9968851
1625	2	0.0031149
1626	1	0.1828687
1626	2	0.8171313
1627	1	0.9992645
1627	2	0.0007355
1628	1	0.9973224
1628	2	0.0026776
1629	1	0.9972693
1629	2	0.0027307
1630	1	0.0977028
1630	2	0.9022972
1631	1	0.2017815
1631	2	0.7982185
1632	1	0.8512195
1632	2	0.1487805
1633	1	0.9976702
1633	2	0.0023298
1634	1	0.1769007
1634	2	0.8230993
1635	1	0.1123166
1635	2	0.8876834
1636	1	0.0011646
1636	2	0.9988354
1637	1	0.9263184
1637	2	0.0736816
1638	1	0.8417246
1638	2	0.1582754
1639	1	0.1875144
1639	2	0.8124856
1640	1	0.1032271
1640	2	0.8967729
1641	1	0.0012918
1641	2	0.9987082
1642	1	0.0009159
1642	2	0.9990841
1643	1	0.2052120
1643	2	0.7947880
1644	1	0.9954896
1644	2	0.0045104
1645	1	0.7342651
1645	2	0.2657349
1646	1	0.0008544
1646	2	0.9991456
1647	1	0.4885825
1647	2	0.5114175
1648	1	0.0740830
1648	2	0.9259170
1649	1	0.0465584
1649	2	0.9534416
1650	1	0.9905582
1650	2	0.0094418
1651	1	0.0466169
1651	2	0.9533831
1652	1	0.1607736
1652	2	0.8392264
1653	1	0.0035992
1653	2	0.9964008
1654	1	0.9087525
1654	2	0.0912475
1655	1	0.0020316
1655	2	0.9979684
1656	1	0.9753661
1656	2	0.0246339
1657	1	0.9968986
1657	2	0.0031014
1658	1	0.9976711
1658	2	0.0023289
1659	1	0.0006907
1659	2	0.9993093
1660	1	0.9916390
1660	2	0.0083610
1661	1	0.1036493
1661	2	0.8963507
1662	1	0.0016336
1662	2	0.9983664
1663	1	0.0043127
1663	2	0.9956873
1664	1	0.9669753
1664	2	0.0330247
1665	1	0.3547771
1665	2	0.6452229
1666	1	0.6424722
1666	2	0.3575278
1667	1	0.0014632
1667	2	0.9985368
1668	1	0.8652631
1668	2	0.1347369
1669	1	0.8081318
1669	2	0.1918682
1670	1	0.9965383
1670	2	0.0034617
1671	1	0.9093163
1671	2	0.0906837
1672	1	0.4655159
1672	2	0.5344841
1673	1	0.9970305
1673	2	0.0029695
1674	1	0.8029786
1674	2	0.1970214
1675	1	0.4045941
1675	2	0.5954059
1676	1	0.4184632
1676	2	0.5815368
1677	1	0.0011645
1677	2	0.9988355
1678	1	0.9985822
1678	2	0.0014178
1679	1	0.1893753
1679	2	0.8106247
1680	1	0.5215982
1680	2	0.4784018
1681	1	0.9989601
1681	2	0.0010399
1682	1	0.8216796
1682	2	0.1783204
1683	1	0.0022796
1683	2	0.9977204
1684	1	0.0437197
1684	2	0.9562803
1685	1	0.2407011
1685	2	0.7592989
1686	1	0.9986328
1686	2	0.0013672
1687	1	0.5269205
1687	2	0.4730795
1688	1	0.2184142
1688	2	0.7815858
1689	1	0.6249783
1689	2	0.3750217
1690	1	0.2047886
1690	2	0.7952114
1691	1	0.1353300
1691	2	0.8646700
1692	1	0.4137425
1692	2	0.5862575
1693	1	0.6295215
1693	2	0.3704785
1694	1	0.4749061
1694	2	0.5250939
1695	1	0.0602106
1695	2	0.9397894
1696	1	0.1227071
1696	2	0.8772929
1697	1	0.1185814
1697	2	0.8814186
1698	1	0.6706950
1698	2	0.3293050
1699	1	0.0009309
1699	2	0.9990691
1700	1	0.0014709
1700	2	0.9985291
1701	1	0.8132537
1701	2	0.1867463
1702	1	0.0418174
1702	2	0.9581826
1703	1	0.0917720
1703	2	0.9082280
1704	1	0.5717349
1704	2	0.4282651
1705	1	0.0033746
1705	2	0.9966254
1706	1	0.0231156
1706	2	0.9768844
1707	1	0.0016974
1707	2	0.9983026
1708	1	0.0017753
1708	2	0.9982247
1709	1	0.0016313
1709	2	0.9983687
1710	1	0.5289956
1710	2	0.4710044
1711	1	0.9989689
1711	2	0.0010311
1712	1	0.6051627
1712	2	0.3948373
1713	1	0.5422096
1713	2	0.4577904
1714	1	0.6707391
1714	2	0.3292609
1715	1	0.4515153
1715	2	0.5484847
1716	1	0.1497403
1716	2	0.8502597
1717	1	0.3358977
1717	2	0.6641023
1718	1	0.9987340
1718	2	0.0012660
1719	1	0.1123336
1719	2	0.8876664
1720	1	0.9994356
1720	2	0.0005644
1721	1	0.6318313
1721	2	0.3681687
1722	1	0.0011117
1722	2	0.9988883
1723	1	0.0007996
1723	2	0.9992004
1724	1	0.9964298
1724	2	0.0035702
1725	1	0.0025354
1725	2	0.9974646
1726	1	0.4925342
1726	2	0.5074658
1727	1	0.4251888
1727	2	0.5748112
1728	1	0.9974105
1728	2	0.0025895
1729	1	0.9970271
1729	2	0.0029729
1730	1	0.9798982
1730	2	0.0201018
1731	1	0.9772891
1731	2	0.0227109
1732	1	0.1432862
1732	2	0.8567138
1733	1	0.9994227
1733	2	0.0005773
1734	1	0.5547904
1734	2	0.4452096
1735	1	0.0027074
1735	2	0.9972926
1736	1	0.1580603
1736	2	0.8419397
1737	1	0.9411076
1737	2	0.0588924
1738	1	0.3890234
1738	2	0.6109766
1739	1	0.9971037
1739	2	0.0028963
1740	1	0.1233526
1740	2	0.8766474
1741	1	0.0006788
1741	2	0.9993212
1742	1	0.6187794
1742	2	0.3812206
1743	1	0.7498515
1743	2	0.2501485
1744	1	0.0008366
1744	2	0.9991634
1745	1	0.1339581
1745	2	0.8660419
1746	1	0.6810516
1746	2	0.3189484
1747	1	0.0755081
1747	2	0.9244919
1748	1	0.1410745
1748	2	0.8589255
1749	1	0.4152613
1749	2	0.5847387
1750	1	0.9991646
1750	2	0.0008354
1751	1	0.1264074
1751	2	0.8735926
1752	1	0.9981271
1752	2	0.0018729
1753	1	0.8908417
1753	2	0.1091583
1754	1	0.0930027
1754	2	0.9069973
1755	1	0.0565357
1755	2	0.9434643
1756	1	0.0402294
1756	2	0.9597706
1757	1	0.1040026
1757	2	0.8959974
1758	1	0.0006021
1758	2	0.9993979
1759	1	0.7773778
1759	2	0.2226222
1760	1	0.8730018
1760	2	0.1269982
1761	1	0.7249183
1761	2	0.2750817
1762	1	0.9982208
1762	2	0.0017792
1763	1	0.0013357
1763	2	0.9986643
1764	1	0.1591608
1764	2	0.8408392
1765	1	0.9991605
1765	2	0.0008395
1766	1	0.9981261
1766	2	0.0018739
1767	1	0.9981855
1767	2	0.0018145
1768	1	0.8463844
1768	2	0.1536156
1769	1	0.0006297
1769	2	0.9993703
1770	1	0.9986942
1770	2	0.0013058
1771	1	0.0038312
1771	2	0.9961688
1772	1	0.9988438
1772	2	0.0011562
1773	1	0.5577373
1773	2	0.4422627
1774	1	0.0100053
1774	2	0.9899947
1775	1	0.3310244
1775	2	0.6689756
1776	1	0.5468892
1776	2	0.4531108
1777	1	0.0007166
1777	2	0.9992834
1778	1	0.9975608
1778	2	0.0024392
1779	1	0.9988299
1779	2	0.0011701
1780	1	0.2705920
1780	2	0.7294080
1781	1	0.0013484
1781	2	0.9986516
1782	1	0.9982318
1782	2	0.0017682
1783	1	0.0482011
1783	2	0.9517989
1784	1	0.8423494
1784	2	0.1576506
1785	1	0.3926058
1785	2	0.6073942
1786	1	0.0019443
1786	2	0.9980557
1787	1	0.2841259
1787	2	0.7158741
1788	1	0.2276243
1788	2	0.7723757
1789	1	0.9981991
1789	2	0.0018009
1790	1	0.9937802
1790	2	0.0062198
1791	1	0.1701535
1791	2	0.8298465
1792	1	0.9992611
1792	2	0.0007389
1793	1	0.1660471
1793	2	0.8339529
1794	1	0.4215534
1794	2	0.5784466
1795	1	0.2085258
1795	2	0.7914742
1796	1	0.0018901
1796	2	0.9981099
1797	1	0.1311037
1797	2	0.8688963
1798	1	0.0009304
1798	2	0.9990696
1799	1	0.0889001
1799	2	0.9110999
1800	1	0.9747550
1800	2	0.0252450
1801	1	0.1747962
1801	2	0.8252038
1802	1	0.1697612
1802	2	0.8302388
1803	1	0.9207399
1803	2	0.0792601
1804	1	0.0012514
1804	2	0.9987486
1805	1	0.5158013
1805	2	0.4841987
1806	1	0.0180284
1806	2	0.9819716
1807	1	0.0028606
1807	2	0.9971394
1808	1	0.0040373
1808	2	0.9959627
1809	1	0.0018570
1809	2	0.9981430
1810	1	0.0017671
1810	2	0.9982329
1811	1	0.4534719
1811	2	0.5465281
1812	1	0.4441140
1812	2	0.5558860
1813	1	0.3666350
1813	2	0.6333650
1814	1	0.1320311
1814	2	0.8679689
1815	1	0.0964078
1815	2	0.9035922
1816	1	0.0019328
1816	2	0.9980672
1817	1	0.5085482
1817	2	0.4914518
1818	1	0.9987142
1818	2	0.0012858
1819	1	0.0019469
1819	2	0.9980531
1820	1	0.0009167
1820	2	0.9990833
1821	1	0.0010746
1821	2	0.9989254
1822	1	0.5538550
1822	2	0.4461450
1823	1	0.1021830
1823	2	0.8978170
1824	1	0.3355228
1824	2	0.6644772
1825	1	0.1177484
1825	2	0.8822516
1826	1	0.1545641
1826	2	0.8454359
1827	1	0.2785764
1827	2	0.7214236
1828	1	0.0403128
1828	2	0.9596872
1829	1	0.1410082
1829	2	0.8589918
1830	1	0.4338702
1830	2	0.5661298
1831	1	0.1123871
1831	2	0.8876129
1832	1	0.0534114
1832	2	0.9465886
1833	1	0.9993319
1833	2	0.0006681
1834	1	0.7532587
1834	2	0.2467413
1835	1	0.9979615
1835	2	0.0020385
1836	1	0.9975570
1836	2	0.0024430
1837	1	0.9983436
1837	2	0.0016564
1838	1	0.2012041
1838	2	0.7987959
1839	1	0.5323314
1839	2	0.4676686
1840	1	0.0011078
1840	2	0.9988922
1841	1	0.6808508
1841	2	0.3191492
1842	1	0.9970335
1842	2	0.0029665
1843	1	0.2450200
1843	2	0.7549800
1844	1	0.7012178
1844	2	0.2987822
1845	1	0.8033136
1845	2	0.1966864
1846	1	0.1860599
1846	2	0.8139401
1847	1	0.0020351
1847	2	0.9979649
1848	1	0.0011324
1848	2	0.9988676
1849	1	0.0019663
1849	2	0.9980337
1850	1	0.5072935
1850	2	0.4927065
1851	1	0.0088000
1851	2	0.9912000
1852	1	0.2242799
1852	2	0.7757201
1853	1	0.2652620
1853	2	0.7347380
1854	1	0.5331003
1854	2	0.4668997
1855	1	0.4877630
1855	2	0.5122370
1856	1	0.6024019
1856	2	0.3975981
1857	1	0.9986272
1857	2	0.0013728
1858	1	0.7459236
1858	2	0.2540764
1859	1	0.0033228
1859	2	0.9966772
1860	1	0.2325961
1860	2	0.7674039
1861	1	0.9986068
1861	2	0.0013932
1862	1	0.9982250
1862	2	0.0017750
1863	1	0.9483077
1863	2	0.0516923
1864	1	0.0011201
1864	2	0.9988799
1865	1	0.9716343
1865	2	0.0283657
1866	1	0.1532923
1866	2	0.8467077
1867	1	0.8872075
1867	2	0.1127925
1868	1	0.0014418
1868	2	0.9985582
1869	1	0.9993200
1869	2	0.0006800
1870	1	0.0009756
1870	2	0.9990244
1871	1	0.0756817
1871	2	0.9243183
1872	1	0.9975512
1872	2	0.0024488
1873	1	0.0900286
1873	2	0.9099714
1874	1	0.1792984
1874	2	0.8207016
1875	1	0.7145536
1875	2	0.2854464
1876	1	0.8515402
1876	2	0.1484598
1877	1	0.9977247
1877	2	0.0022753
1878	1	0.7561325
1878	2	0.2438675
1879	1	0.1770664
1879	2	0.8229336
1880	1	0.2606003
1880	2	0.7393997
1881	1	0.8255102
1881	2	0.1744898
1882	1	0.6509563
1882	2	0.3490437
1883	1	0.6199081
1883	2	0.3800919
1884	1	0.7112824
1884	2	0.2887176
1885	1	0.0009788
1885	2	0.9990212
1886	1	0.8958532
1886	2	0.1041468
1887	1	0.5688753
1887	2	0.4311247
1888	1	0.6958494
1888	2	0.3041506
1889	1	0.0031338
1889	2	0.9968662
1890	1	0.5678974
1890	2	0.4321026
1891	1	0.2159254
1891	2	0.7840746
1892	1	0.0009530
1892	2	0.9990470
1893	1	0.0005803
1893	2	0.9994197
1894	1	0.0004988
1894	2	0.9995012
1895	1	0.1548299
1895	2	0.8451701
1896	1	0.4680394
1896	2	0.5319606
1897	1	0.6814792
1897	2	0.3185208
1898	1	0.0014361
1898	2	0.9985639
1899	1	0.9962685
1899	2	0.0037315
1900	1	0.3819415
1900	2	0.6180585
1901	1	0.5273892
1901	2	0.4726108
1902	1	0.9987437
1902	2	0.0012563
1903	1	0.0038761
1903	2	0.9961239
1904	1	0.9977838
1904	2	0.0022162
1905	1	0.4058112
1905	2	0.5941888
1906	1	0.0017676
1906	2	0.9982324
1907	1	0.0018695
1907	2	0.9981305
1908	1	0.5441868
1908	2	0.4558132
1909	1	0.5780873
1909	2	0.4219127
1910	1	0.1358530
1910	2	0.8641470
1911	1	0.9676276
1911	2	0.0323724
1912	1	0.7871126
1912	2	0.2128874
1913	1	0.7437638
1913	2	0.2562362
1914	1	0.9991413
1914	2	0.0008587
1915	1	0.0008212
1915	2	0.9991788
1916	1	0.0006712
1916	2	0.9993288
1917	1	0.0005323
1917	2	0.9994677
1918	1	0.3371551
1918	2	0.6628449
1919	1	0.7890812
1919	2	0.2109188
1920	1	0.0296235
1920	2	0.9703765
1921	1	0.0008259
1921	2	0.9991741
1922	1	0.1278964
1922	2	0.8721036
1923	1	0.9989367
1923	2	0.0010633
1924	1	0.0246034
1924	2	0.9753966
1925	1	0.0009123
1925	2	0.9990877
1926	1	0.9985426
1926	2	0.0014574
1927	1	0.0484455
1927	2	0.9515545
1928	1	0.0653388
1928	2	0.9346612
1929	1	0.9772891
1929	2	0.0227109
1930	1	0.8678843
1930	2	0.1321157
1931	1	0.9989486
1931	2	0.0010514
1932	1	0.9990359
1932	2	0.0009641
1933	1	0.5188593
1933	2	0.4811407
1934	1	0.2001939
1934	2	0.7998061
1935	1	0.7157389
1935	2	0.2842611
1936	1	0.2578041
1936	2	0.7421959
1937	1	0.4839569
1937	2	0.5160431
1938	1	0.6857143
1938	2	0.3142857
1939	1	0.7451567
1939	2	0.2548433
1940	1	0.0015239
1940	2	0.9984761
1941	1	0.0708497
1941	2	0.9291503
1942	1	0.9968116
1942	2	0.0031884
1943	1	0.9854037
1943	2	0.0145963
1944	1	0.7082038
1944	2	0.2917962
1945	1	0.1625454
1945	2	0.8374546
1946	1	0.7414403
1946	2	0.2585597
1947	1	0.9969895
1947	2	0.0030105
1948	1	0.0618257
1948	2	0.9381743
1949	1	0.9969470
1949	2	0.0030530
1950	1	0.6354512
1950	2	0.3645488
1951	1	0.7004215
1951	2	0.2995785
1952	1	0.9975881
1952	2	0.0024119
1953	1	0.0016093
1953	2	0.9983907
1954	1	0.0036917
1954	2	0.9963083
1955	1	0.0031101
1955	2	0.9968899
1956	1	0.0999149
1956	2	0.9000851
1957	1	0.9989907
1957	2	0.0010093
1958	1	0.5011034
1958	2	0.4988966
1959	1	0.0040300
1959	2	0.9959700
1960	1	0.0005298
1960	2	0.9994702
1961	1	0.3003543
1961	2	0.6996457
1962	1	0.9975032
1962	2	0.0024968
1963	1	0.2406276
1963	2	0.7593724
1964	1	0.0430737
1964	2	0.9569263
1965	1	0.9967696
1965	2	0.0032304
1966	1	0.9884477
1966	2	0.0115523
1967	1	0.1776851
1967	2	0.8223149
1968	1	0.8387075
1968	2	0.1612925
1969	1	0.9964061
1969	2	0.0035939
1970	1	0.0010798
1970	2	0.9989202
1971	1	0.9958754
1971	2	0.0041246
1972	1	0.0509160
1972	2	0.9490840
1973	1	0.1830954
1973	2	0.8169046
1974	1	0.9190994
1974	2	0.0809006
1975	1	0.9988519
1975	2	0.0011481
1976	1	0.2653502
1976	2	0.7346498
1977	1	0.2711980
1977	2	0.7288020
1978	1	0.5513813
1978	2	0.4486187
1979	1	0.3816186
1979	2	0.6183814
1980	1	0.0492664
1980	2	0.9507336
1981	1	0.3682350
1981	2	0.6317650
1982	1	0.0622813
1982	2	0.9377187
1983	1	0.0006935
1983	2	0.9993065
1984	1	0.2518257
1984	2	0.7481743
1985	1	0.0025831
1985	2	0.9974169
1986	1	0.9274625
1986	2	0.0725375
1987	1	0.1934048
1987	2	0.8065952
1988	1	0.6898785
1988	2	0.3101215
1989	1	0.8747910
1989	2	0.1252090
1990	1	0.0008667
1990	2	0.9991333
1991	1	0.7429168
1991	2	0.2570832
1992	1	0.0027638
1992	2	0.9972362
1993	1	0.9946773
1993	2	0.0053227
1994	1	0.1983231
1994	2	0.8016769
1995	1	0.8273388
1995	2	0.1726612
1996	1	0.9044263
1996	2	0.0955737
1997	1	0.8358943
1997	2	0.1641057
1998	1	0.7161074
1998	2	0.2838926
1999	1	0.3425451
1999	2	0.6574549
2000	1	0.0366877
2000	2	0.9633123
2001	1	0.0018693
2001	2	0.9981307
2002	1	0.1989716
2002	2	0.8010284
2003	1	0.4579488
2003	2	0.5420512
2004	1	0.9355794
2004	2	0.0644206
2005	1	0.5569705
2005	2	0.4430295
2006	1	0.9073128
2006	2	0.0926872
2007	1	0.4032143
2007	2	0.5967857
2008	1	0.4906151
2008	2	0.5093849
2009	1	0.0207946
2009	2	0.9792054
2010	1	0.9977985
2010	2	0.0022015
2011	1	0.7194619
2011	2	0.2805381
2012	1	0.8062987
2012	2	0.1937013
2013	1	0.2688356
2013	2	0.7311644
2014	1	0.7042710
2014	2	0.2957290
2015	1	0.8876681
2015	2	0.1123319
2016	1	0.0011951
2016	2	0.9988049
2017	1	0.3366084
2017	2	0.6633916
2018	1	0.0015170
2018	2	0.9984830
2019	1	0.9494978
2019	2	0.0505022
2020	1	0.1002517
2020	2	0.8997483
2021	1	0.3055490
2021	2	0.6944510
2022	1	0.9965621
2022	2	0.0034379
2023	1	0.0006445
2023	2	0.9993555
2024	1	0.9977667
2024	2	0.0022333
2025	1	0.9968968
2025	2	0.0031032
2026	1	0.3534577
2026	2	0.6465423
2027	1	0.5845897
2027	2	0.4154103
2028	1	0.9994624
2028	2	0.0005376
2029	1	0.0036699
2029	2	0.9963301
2030	1	0.0009039
2030	2	0.9990961
2031	1	0.9768534
2031	2	0.0231466
2032	1	0.0074511
2032	2	0.9925489
2033	1	0.9994814
2033	2	0.0005186
2034	1	0.3997124
2034	2	0.6002876
2035	1	0.0013464
2035	2	0.9986536
2036	1	0.5658165
2036	2	0.4341835
2037	1	0.3929659
2037	2	0.6070341
2038	1	0.0023639
2038	2	0.9976361
2039	1	0.1653724
2039	2	0.8346276
2040	1	0.1021729
2040	2	0.8978271
2041	1	0.7673295
2041	2	0.2326705
2042	1	0.1442902
2042	2	0.8557098
2043	1	0.9979134
2043	2	0.0020866
2044	1	0.7095875
2044	2	0.2904125
2045	1	0.0014733
2045	2	0.9985267
2046	1	0.0011090
2046	2	0.9988910
2047	1	0.9990315
2047	2	0.0009685
2048	1	0.4915645
2048	2	0.5084355
2049	1	0.0010345
2049	2	0.9989655
2050	1	0.8386236
2050	2	0.1613764
2051	1	0.0499899
2051	2	0.9500101
2052	1	0.0017027
2052	2	0.9982973
2053	1	0.0148088
2053	2	0.9851912
2054	1	0.9834046
2054	2	0.0165954
2055	1	0.0037989
2055	2	0.9962011
2056	1	0.9968778
2056	2	0.0031222
2057	1	0.0014759
2057	2	0.9985241
2058	1	0.7016103
2058	2	0.2983897
2059	1	0.0461718
2059	2	0.9538282
2060	1	0.1778209
2060	2	0.8221791
2061	1	0.2775227
2061	2	0.7224773
2062	1	0.3463987
2062	2	0.6536013
2063	1	0.9410214
2063	2	0.0589786
2064	1	0.6203421
2064	2	0.3796579
2065	1	0.0015469
2065	2	0.9984531
2066	1	0.1788012
2066	2	0.8211988
2067	1	0.0033668
2067	2	0.9966332
2068	1	0.7667242
2068	2	0.2332758
2069	1	0.0009967
2069	2	0.9990033
2070	1	0.1097034
2070	2	0.8902966
2071	1	0.2692324
2071	2	0.7307676
2072	1	0.9985662
2072	2	0.0014338
2073	1	0.0006762
2073	2	0.9993238
2074	1	0.1220063
2074	2	0.8779937
2075	1	0.9568500
2075	2	0.0431500
2076	1	0.2287728
2076	2	0.7712272
2077	1	0.0014751
2077	2	0.9985249
2078	1	0.9112473
2078	2	0.0887527
2079	1	0.0024073
2079	2	0.9975927
2080	1	0.3861386
2080	2	0.6138614
2081	1	0.0006202
2081	2	0.9993798
2082	1	0.7595131
2082	2	0.2404869
2083	1	0.9956332
2083	2	0.0043668
2084	1	0.7600395
2084	2	0.2399605
2085	1	0.1227023
2085	2	0.8772977
2086	1	0.9975828
2086	2	0.0024172
2087	1	0.0676150
2087	2	0.9323850
2088	1	0.0005937
2088	2	0.9994063
2089	1	0.5674981
2089	2	0.4325019
2090	1	0.9428495
2090	2	0.0571505
2091	1	0.0007605
2091	2	0.9992395
2092	1	0.0861380
2092	2	0.9138620
2093	1	0.2253558
2093	2	0.7746442
2094	1	0.5039165
2094	2	0.4960835
2095	1	0.0005357
2095	2	0.9994643
2096	1	0.5774561
2096	2	0.4225439
2097	1	0.9989204
2097	2	0.0010796
2098	1	0.0081503
2098	2	0.9918497
2099	1	0.9962758
2099	2	0.0037242
2100	1	0.0012992
2100	2	0.9987008
2101	1	0.0509889
2101	2	0.9490111
2102	1	0.2423356
2102	2	0.7576644
2103	1	0.2275970
2103	2	0.7724030
2104	1	0.4162695
2104	2	0.5837305
2105	1	0.6587155
2105	2	0.3412845
2106	1	0.0008615
2106	2	0.9991385
2107	1	0.9967032
2107	2	0.0032968
2108	1	0.3778932
2108	2	0.6221068
2109	1	0.1594041
2109	2	0.8405959
2110	1	0.7243414
2110	2	0.2756586
2111	1	0.2923460
2111	2	0.7076540
2112	1	0.9703128
2112	2	0.0296872
2113	1	0.1511871
2113	2	0.8488129
2114	1	0.1006747
2114	2	0.8993253
2115	1	0.5015087
2115	2	0.4984913
2116	1	0.3863880
2116	2	0.6136120
2117	1	0.2594812
2117	2	0.7405188
2118	1	0.6022616
2118	2	0.3977384
2119	1	0.9983856
2119	2	0.0016144
2120	1	0.0012099
2120	2	0.9987901
2121	1	0.3101571
2121	2	0.6898429
2122	1	0.0010778
2122	2	0.9989222
2123	1	0.7505358
2123	2	0.2494642
2124	1	0.7798271
2124	2	0.2201729
2125	1	0.3286850
2125	2	0.6713150
2126	1	0.0014264
2126	2	0.9985736
2127	1	0.0318965
2127	2	0.9681035
2128	1	0.0022867
2128	2	0.9977133
2129	1	0.9928669
2129	2	0.0071331
2130	1	0.9953436
2130	2	0.0046564
2131	1	0.0008670
2131	2	0.9991330
2132	1	0.2717404
2132	2	0.7282596
2133	1	0.9982361
2133	2	0.0017639
2134	1	0.4533618
2134	2	0.5466382
2135	1	0.2467627
2135	2	0.7532373
2136	1	0.5579563
2136	2	0.4420437
2137	1	0.1117956
2137	2	0.8882044
2138	1	0.9699554
2138	2	0.0300446
2139	1	0.0217532
2139	2	0.9782468
2140	1	0.2490154
2140	2	0.7509846
2141	1	0.4388072
2141	2	0.5611928
2142	1	0.8018381
2142	2	0.1981619
2143	1	0.0547699
2143	2	0.9452301
2144	1	0.2919778
2144	2	0.7080222
2145	1	0.9988611
2145	2	0.0011389
2146	1	0.6769461
2146	2	0.3230539
2147	1	0.0006821
2147	2	0.9993179
2148	1	0.9990600
2148	2	0.0009400
2149	1	0.9963880
2149	2	0.0036120
2150	1	0.2055449
2150	2	0.7944551
2151	1	0.9992445
2151	2	0.0007555
2152	1	0.0013990
2152	2	0.9986010
2153	1	0.9327884
2153	2	0.0672116
2154	1	0.7696447
2154	2	0.2303553
2155	1	0.0044184
2155	2	0.9955816
2156	1	0.8791344
2156	2	0.1208656
2157	1	0.7247708
2157	2	0.2752292
2158	1	0.1162869
2158	2	0.8837131
2159	1	0.0951943
2159	2	0.9048057
2160	1	0.0242717
2160	2	0.9757283
2161	1	0.0028995
2161	2	0.9971005
2162	1	0.9967035
2162	2	0.0032965
2163	1	0.3069074
2163	2	0.6930926
2164	1	0.1142309
2164	2	0.8857691
2165	1	0.0016962
2165	2	0.9983038
2166	1	0.3125352
2166	2	0.6874648
2167	1	0.0037304
2167	2	0.9962696
2168	1	0.9985162
2168	2	0.0014838
2169	1	0.8833651
2169	2	0.1166349
2170	1	0.1950431
2170	2	0.8049569
2171	1	0.4902608
2171	2	0.5097392
2172	1	0.2528628
2172	2	0.7471372
2173	1	0.9988506
2173	2	0.0011494
2174	1	0.2473516
2174	2	0.7526484
2175	1	0.9970748
2175	2	0.0029252
2176	1	0.4904220
2176	2	0.5095780
2177	1	0.9983734
2177	2	0.0016266
2178	1	0.9826843
2178	2	0.0173157
2179	1	0.1095111
2179	2	0.8904889
2180	1	0.9964174
2180	2	0.0035826
2181	1	0.9982521
2181	2	0.0017479
2182	1	0.8682632
2182	2	0.1317368
2183	1	0.7099795
2183	2	0.2900205
2184	1	0.8217145
2184	2	0.1782855
2185	1	0.1630826
2185	2	0.8369174
2186	1	0.9976258
2186	2	0.0023742
2187	1	0.4704603
2187	2	0.5295397
2188	1	0.1688351
2188	2	0.8311649
2189	1	0.1135460
2189	2	0.8864540
2190	1	0.9982654
2190	2	0.0017346
2191	1	0.4231995
2191	2	0.5768005
2192	1	0.0417234
2192	2	0.9582766
2193	1	0.0014770
2193	2	0.9985230
2194	1	0.3937365
2194	2	0.6062635
2195	1	0.7347173
2195	2	0.2652827
2196	1	0.9990446
2196	2	0.0009554
2197	1	0.9972350
2197	2	0.0027650
2198	1	0.9962012
2198	2	0.0037988
2199	1	0.9974503
2199	2	0.0025497
2200	1	0.6693511
2200	2	0.3306489
2201	1	0.0006172
2201	2	0.9993828
2202	1	0.2574151
2202	2	0.7425849
2203	1	0.9141922
2203	2	0.0858078
2204	1	0.0011852
2204	2	0.9988148
2205	1	0.1022311
2205	2	0.8977689
2206	1	0.0016296
2206	2	0.9983704
2207	1	0.3485956
2207	2	0.6514044
2208	1	0.3471464
2208	2	0.6528536
2209	1	0.1947241
2209	2	0.8052759
2210	1	0.9977936
2210	2	0.0022064
2211	1	0.5145979
2211	2	0.4854021
2212	1	0.2262607
2212	2	0.7737393
2213	1	0.6871701
2213	2	0.3128299
2214	1	0.9731414
2214	2	0.0268586
2215	1	0.0016108
2215	2	0.9983892
2216	1	0.4600580
2216	2	0.5399420
2217	1	0.0137289
2217	2	0.9862711
2218	1	0.3173113
2218	2	0.6826887
2219	1	0.9994701
2219	2	0.0005299
2220	1	0.3145587
2220	2	0.6854413
2221	1	0.5894141
2221	2	0.4105859
2222	1	0.5022471
2222	2	0.4977529
2223	1	0.4278817
2223	2	0.5721183
2224	1	0.0018471
2224	2	0.9981529
2225	1	0.0256444
2225	2	0.9743556
2226	1	0.4673791
2226	2	0.5326209
2227	1	0.9983248
2227	2	0.0016752
2228	1	0.2123862
2228	2	0.7876138
2229	1	0.1598188
2229	2	0.8401812
2230	1	0.8798912
2230	2	0.1201088
2231	1	0.0010877
2231	2	0.9989123
2232	1	0.9983908
2232	2	0.0016092
2233	1	0.4678134
2233	2	0.5321866
2234	1	0.9993522
2234	2	0.0006478
2235	1	0.0760146
2235	2	0.9239854
2236	1	0.2719584
2236	2	0.7280416
2237	1	0.2226268
2237	2	0.7773732
2238	1	0.1705999
2238	2	0.8294001
2239	1	0.9981672
2239	2	0.0018328
2240	1	0.6325998
2240	2	0.3674002
2241	1	0.0296735
2241	2	0.9703265
2242	1	0.9981244
2242	2	0.0018756
2243	1	0.9983112
2243	2	0.0016888
2244	1	0.1173408
2244	2	0.8826592
2245	1	0.7225991
2245	2	0.2774009
2246	1	0.9968596
2246	2	0.0031404

Cada um desses valores é uma proporção estimada de palavras de cada documento que foram geradas a partir de cada tópico. Por exemplo, o modelo estima que apenas cerca de 24,8% das palavras no documento 1 foram geradas a partir do tópico 1. Podemos ver que o documento 6 foi tirado quase inteiramente do tópico 2. Para verificar, podemos tidy() a DFM e checar quais as palavras comuns nesse documento.

tidy(AssociatedPress) %>%
  filter(document == 6) %>%
  arrange(desc(count))

## # A tibble: 287 × 3
##    document term           count
##       <int> <chr>          <dbl>
##  1        6 noriega           16
##  2        6 panama            12
##  3        6 jackson            6
##  4        6 powell             6
##  5        6 administration     5
##  6        6 economic           5
##  7        6 general            5
##  8        6 i                  5
##  9        6 panamanian         5
## 10        6 american           4
## # … with 277 more rows

9.2.1.2 Expressed Agenda Model

Projetado para medir como os autores dividem sua atenção sobre temas, o modelo apresenta outra maneira de explorar a mesma estrutura inaugurada pelo LDA⁸. Sua principal suposição é que cada autor divide sua atenção a um conjunto de tópicos. Assim, condicionado a tal distribuição de atenção dos atores, o tópico de cada documento é extraído.

O Expressed Agenda Model foi utilizado por Moreira (2020) para o caso brasileiro com o objetivo de identificar as ênfases temáticas proferidas pelos deputados e deputadas federais em seus discursos no Pequeno Expediente de 1999 a 2014. Com foco sobre a 54 legislatura, veremos a aplicação.

9.2.1.2.1 1. Definição do número de tópicos

Uma vez realizado o pré-processamento dos dados, o primeiro desafio imposto pelo modelo é a definição do número $k$ de tópicos presente no corpus, ou seja, a quantidade de temas abordados em cada uma das legislaturas analisadas. Para a definição do número $k$ de tópicos, duas estratégias foram utilizadas:

o uso de um modelo não paramétrico para clusterização de texto baseado no Dirichlet Process Prior Grimmer (2010);

O modelo não paramético resultou em 36 tópicos contidos no acervo. No entanto, dadas as ponderações já apresentadas, esse resultado não foi considerado de forma definitiva e a estipulação da quantidade $k$ de tópicos contou com uma avaliação qualitativa do resultado de diferentes modelos para cada legislatura.

a estimação de diferentes modelos.

A avaliação qualitativa permite que o valor $k$ seja definido pela coesão substantiva identificada pelo analista através da análise dos stems mais associados a cada tópico em diferentes modelos e da leitura de amostras aleatórias de documentos presentes nas categorias estimadas por cada modelo. Foram estimados e analisados os resultados de modelos que variaram de 5 a 80 tópicos.

Por um lado, comparados entre si, quanto menor o número de tópicos de um modelo, maior é a diversidade de discursos classificados em cada um, resultando em categorias muito genéricas. Por outro, quanto maior o número de tópicos do modelo, maior é a quantidade de tópicos tratando sobre o mesmo tema. Por essa razão, com o auxílio da evidência estatística do modelo não paramétrico, foi possível analisar de forma qualitativa os resultados dos 75 modelos estimados para a definição de um resultado de 39 tópicos para a legislatura 54.

O resultado do modelo com as 39 categorias podem ser encontrados na Figura 9.3 Moreira (2020).

Figure 9.3: Temas dos Discursos Proferidos na Legislatura 54

Na primeira coluna apresenta-se o rótulo dado a cada tópico após a leitura de uma amostra de ao menos dez discursos aleatoriamente selecionados de cada um deles. Na segunda coluna, é possível verificar até o quinto stem com maior informação mútua em cada tópico. Na terceira, é apresentado o percentual de documentos do corpus classificado em cada um dos tópicos.

9.2.1.2.2 Validação

Distintas formas de validação foram adotadas para averiguar se os resultados são substantivamente relevantes. Os tópicos foram validados por meio de quatro procedimentos:

dado que a matéria-prima para a análise dos tópicos são os stems das palavras contidas nos discursos, verificou-se quais os dez stems mais associados a cada um pelo do cálculo de sua informação mútua Grimmer and Stewart (2013);
foram lidos ao menos dez discursos aleatoriamente selecionados para rotulação de cada tópico;
sendo cada discurso pertencente a um tópico, é analisada sua pertinência temporal conforme a frequência dos tópicos ao longo da legislatura;
é qualitativamente analisada a dedicação de parlamentares selecionados a tópicos específicos, de modo que seja possível identificar se há coerência entre a classificação temática dos discuros e perfis parlamentares amplamente conhecidos e difundidos na sociedade e na ciência política brasileira.

Os resultados de dois dos quatro procedimentos de validação: a leitura atenta de uma amostra aleatória dos discursos presentes em cada tópico para rotulagem adequada e a análise de raízes com a maior informação mútua em cada um dos tópicos podem ser encontrados na Tabela apresentada.

Avançamos a seguir, portanto, no sentido de avaliar a pertinência temporal dos tópicos e a ênfase temática esperada de alguns parlamentares de perfil amplamente conhecido pela sociedade brasileira e a ciência política nacional.

Pertinência temporal de topicos selecionados: verifica-se se os discursos relacionados a cada tópico estão em acordo com debates desenvolvidos ao longo da legislatura e, em especial, se condizem com a ocorrência de eventos exógenos à instância de produção do discurso. Na figura a seguir, vemos a relevância do tema “ESPORTE” ao longo da legislatura analisada.

Ênfase temática de deputados federais selecionados: A principal contribuição do expressed agenda model, em comparação com as demais metodologias utlizadas na classificação de conteúdo de forma não supervisionada, é sua estrutura hierárquica que permite identificar a ênfase de temática de autores. Por tal razão, como última estratégia de validação dos resultados obtidos pelo modelo para as legislaturas analisadas, foram averiguadas as ênfases temáticas de deputados federais cujo perfil é amplamente difundido e conhecido na ciência política nacional.

Como se pode constatar, as falas proferidas pelo então deputado federal Romário possuem nítida relação com sua atuação parlamentar.

Em conjunto com os outros dois procedimentos de validação, a ênfase temática dos deputados federais identificada pelo Expressed Agenda Model e ilustrada com a apresentação dos resultados para o tema do esporte ao longo da legislatura e os pronunciamentos do deputado federal Romário indica que o modelo estimado foi satisfatório.

9.2.1.3 STM: Structural Topic Model:

A cientista política Margaret E. Roberts é a principal desenvolvedora do STM. Em conjunto com Brandon Stewart e Dustin Tingley ganhou o Political Methodology Society’s Statistical Software Award for 2018.

Com base na mesma estrutura do LDA, o STM inova com duas características. Em primeiro lugar, permite que, ao nível dos documentos, seus metadados⁹, entendidos enquanto covariáveis, sejam incorporados ao modelo. Logo, informações como autoria e data de publicação podem ser incluídas para contribuir com a estimação dos tópicos. Em segundo lugar, permite estimar a correlação entre os tópicos de modo a identificar, por exemplo, quando dois tópicos podem ocorrer simultaneamente num documento. Tal informação pode auxiliar o pesquisador na identificação de temas que transcendam os tópicos e, portanto, permita sua aglutinação.

Assim, com o objetivo de estimar a relação entre metadados e tópicos, no STM, estes são definidos como uma mistura sobre palavras em que cada palavra tem uma probabilidade de pertencer a um tópico. Logo, um documento é uma mistura sobre tópicos, o que significa que um único documento pode ser composto de vários tópicos, do mesmo modo como no caso do LDA.

O pacote stm disponível no R possui a seguinte estrutura heurística:

Vejamos uma aplicação do STM com base no seu tutorial e num acervo de postagens de blogs sobre a política americana¹⁰. Como covariável para estimação dos tópicos dos documentos, será utilizada blog ideology (rating) em conjunto com a ordem cronológica dos dias. Para realizar o exemplo, será necessário baixar os dados, disponível no link do tutorial do pacote.

# carregando pacotes ----
library(stm)        # Para modelagem de tópico estruturada
library(igraph)     # Para análise de rede e visualização
library(stmCorrViz) # Para visualizar a correlação hierarquica do STMs

Para preparação do corpus a função textProcessor realiza a retirada de stopwords e realiza o processo de steeming na base.

processed <- textProcessor(data$documents, metadata=data)

Utilizamos então a função prepDocuments para estruturar os dados para modelagem de tópico. O objeto não deve possuir valores faltantes. Palavras de baixa frequência podem ser removidas usando a opção lower.tresh.

out <- prepDocuments(processed$documents, processed$vocab, processed$meta)

Em seguida, salvamos o resultado do objeto, os documentos e vocabulário em variáveis:

docs <- out$documents
vocab <- out$vocab
meta <- out$meta

Para checar quantas palavras e documentos seriam removidos utilizando diferentes lower thresholds, a função plotRemoved() pode ser utilizada para visualização:

# associando texto com meta-dados ----
plotRemoved(processed$documents, lower.thresh=seq(1,200, by=100))

9.2.1.3.1 Estimação com a prevalência tópica

A prevalência tópica captura quanto cada tópico contribui para um documento. Como documentos diferentes vêm de fontes diferentes, é natural querer permitir que essa prevalência varie com os metadados que temos sobre as origens dos documentos. Para isso, executamos o modelo stm usando os dados de out com 20 tópicos. Aqui verificamos como a prevalência de tópico varia de acordo com os documentos e metadados. O pacote stm necessita que coloquemos o numéro máximo de expectation-maximization iterations $=$ 75, e um seed para reproducibilidade.

poliblogPrevFit <- stm(out$documents, out$vocab, K=20, prevalence=~rating+s(day), 
                       max.em.its=75, data=out$meta, init.type="Spectral", 
                       seed=8458159)

9.2.1.3.2 Interpretando o STM: visualizando e inspecionando os resultados

Há muitas maneiras de investigar os resultados do modelo, como inspecionar as palavras associadas aos tópicos ou a relação entre metadados e tópicos. O pacote stm fornece diferentes opções/funções prontas para esse uso.

Exibindo palavras associadas a tópicos (labelTopics, plot.STM(, type = "labels"), sageLabels, plot.STM(, type = "perspectives")) ou documentos altamente associados a tópicos específicos (findThoughts, plotQuote).

# palavras mais associadas ----

labelTopics(poliblogPrevFit, c(3, 7, 20))

## Topic 3 Top Words:
##       Highest Prob: obama, democrat, poll, hillari, voter, state, vote 
##       FREX: superdeleg, deleg, primari, poll, pennsylvania, michigan, hampshir 
##       Lift: “expanded”, bredesen, callupd, caucus-go, cfi, enthus, errorth 
##       Score: hillari, poll, obama, mccain, voter, clinton, deleg 
## Topic 7 Top Words:
##       Highest Prob: obama, barack, campaign, senat, said, will, clinton 
##       FREX: blagojevich, barack, obama, illinoi, blago, lieberman, president-elect 
##       Lift: -watch, “fg, “transformational”, accommodationist, addedso, anti-lieberman, anti-nafta 
##       Score: obama, barack, blagojevich, hillari, campaign, clinton, lieberman 
## Topic 20 Top Words:
##       Highest Prob: will, american, america, countri, presid, nation, peopl 
##       FREX: america, countri, togeth, veteran, centuri, proud, must 
##       Lift: --young, amar, antietam, borderland, century…, crosscurr, doughboy 
##       Score: america, countri, presid, veteran, war, american, world

Por padrão, a função retorna o resultado de diferentes medidas, são elas:

Highest Prob: palavras com maior probabilidade;
FREX: pondera as palavras pela sua frequência geral e como elas são exclusivas para o tópico.
Lift: pondera as palavras dividindo por sua frequência em outros tópicos, dando, portanto, maior peso às palavras que aparecem com menos frequência em outros tópicos.
Score: divide o log da frequência da palavra no tópico pelo log de sua freqüência em outros tópicos.

Para examinar documentos altamente associados a tópicos, a função findThoughts pode ser usada. A função retorna os documentos altamente associados a cada tópico. A leitura desses documentos é útil para entender o conteúdo de um tópico e interpretar seu significado.

# documentos mais associados
thoughts3 <- findThoughts(poliblogPrevFit, texts = shortdoc,
                          n = 2, topics = 3)$docs[[1]]
thoughts20 <- findThoughts(poliblogPrevFit, texts = shortdoc,
                           n = 2, topics = 20)$docs[[1]]
par(mfrow = c(1, 2),mar = c(.5, .5, 1, .5))
plotQuote(thoughts3, width = 30, main = "Topic 3")
plotQuote(thoughts20, width = 30, main = "Topic 20")

Relacionamentos entre metadados e tópicos (estimateEffect). Essas relações podem desempenhar um papel fundamental na validação do modelo Ying, Montgomery, and Stewart (2022).

# Relacionamentos entre metadados e tópicos ----

out$meta$rating <- as.factor(out$meta$rating)
prep <- estimateEffect(1:20 ~ rating + s(day), poliblogPrevFit, meta = out$meta,
                       uncertainty = "Global")

summary(prep, topics=1)

## 
## Call:
## estimateEffect(formula = 1:20 ~ rating + s(day), stmobj = poliblogPrevFit, 
##     metadata = out$meta, uncertainty = "Global")
## 
## 
## Topic 1:
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.004059   0.009114   0.445 0.656070    
## ratingLiberal 0.020746   0.002346   8.842  < 2e-16 ***
## s(day)1       0.041205   0.017320   2.379 0.017376 *  
## s(day)2       0.019688   0.011140   1.767 0.077188 .  
## s(day)3       0.006901   0.012429   0.555 0.578753    
## s(day)4       0.032070   0.011077   2.895 0.003795 ** 
## s(day)5       0.034652   0.011664   2.971 0.002976 ** 
## s(day)6       0.013289   0.011664   1.139 0.254617    
## s(day)7       0.043176   0.011248   3.838 0.000124 ***
## s(day)8       0.059298   0.015728   3.770 0.000164 ***
## s(day)9       0.024080   0.014236   1.692 0.090759 .  
## s(day)10      0.015492   0.014222   1.089 0.276045    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Para ver como a prevalência de tópicos difere entre os valores de uma covariável categórica:

plot(prep, covariate="rating", topics=c(3, 7, 20), model=poliblogPrevFit, 
     method="difference", cov.value1="Liberal", cov.value2="Conservative",
     xlab="More Conservative ... More Liberal", 
     main="Effect of Liberal vs. Conservative",
     xlim=c(-.15,.15), labeltype ="custom", custom.labels=c('Obama', 'Sarah Palin', 
                                                          'Bush Presidency'))

Calculando correlações dos tópicos (topicCorr). Podemos também visualizar a correlação entre tópicos, correlações positivas entre tópicos indicam que ambos os tópicos provavelmente serão discutidos em um documento. A rede de relações demonstra o quão próximos os tópicos são entre sí, ou seja o quão provavél serão discutidos em um documento.

mod.out.corr <- topicCorr(poliblogPrevFit)
plot(mod.out.corr)

9.2.1.3.3 STM - Comunicando os resultados

Corpus: A visualização no nível do corpus pode ser feita em relação à proporção esperada de cada tópico no corpus.

# proporção esperada de cada tópico no corpus ----
plot(poliblogPrevFit, type = "summary", xlim = c(0, .3))

Vemos no exemplo que o tópico Sarah Palin / Vice-President (7) tem pequena presença no corpus. O tópico mais comum é um tópico geral cheio de palavras que os blogueiros geralmente usam e, portanto, não é muito interpretável. As palavras listadas na figura são as três principais palavras associadas ao tópico.

Relação Metadado/tópico: A capacidade de estimar relacionamentos de metadados/tópicos é uma vantagem central do modelo STM. Quando a covariável de interesse é binária, há interesse em um contraste particular. No exemplo a seguir, a opção method = “difference” irá traçar a mudança na proporção do tópico mudando de um valor específico para outro.

# Relação Metadata/topico: ----
plot(prep, covariate = "rating", topics = c(3, 7, 20),
     model = poliblogPrevFit, method = "difference",
     cov.value1 = "Liberal", cov.value2 = "Conservative",
     xlab = "More Conservative ... More Liberal",
     main = "Effect of Liberal vs. Conservative",
     xlim = c(-.1, .1), labeltype = "custom",
     custom.labels = c('Obama', 'Sarah Palin','Bush Presidency'))

Vemos que o Tópico 1 é fortemente usado pelos conservadores em comparação aos liberais, enquanto o Tópico 7 está próximo do meio, mas ainda conservador. O tópico 10, relacionado a Bush, foi amplamente associado a escritores liberais, o que está de acordo com a tendência observada de distanciamento conservador de Bush após sua presidência.

Para tratar variáveis contínuas, pode-se assumir um ajuste linear ou usar splines. No exemplo anterior, permitimos que a variável day tivesse um relacionamento não linear no estágio de estimativa de tópico. Podemos então traçar o seu efeito nos tópicos. Abaixo, plotamos a relação entre o tempo e o tópico vicepresidencial, tópico 7. O tópico tem um pico quando Sarah Palin se tornou companheira de chapa de John McCain no final de agosto de 2008.

# Relação Metadata/topico: ----
plot(prep, "day", method = "continuous", topics = 7,
     model = z, printlegend = FALSE, xaxt = "n", xlab = "Time (2008)")
monthseq <- seq(from = as.Date("2008-01-01"),
                to = as.Date("2008-12-01"), by = "month")
monthnames <- months(monthseq)
axis(1,at = as.numeric(monthseq) - min(as.numeric(monthseq)), labels = monthnames)

Plotando a interação entre covariáveis: Neste exemplo, reestimamos o STM para permitir uma interação entre o day (inserido linearmente) e rating. Exibimos os resultados na figura para o tópico 20 (administração Bush). Observamos que os conservadores nunca escreveram muito sobre esse assunto, enquanto os liberais discutiram muito esse tópico, mas com o tempo o assunto diminuiu em importância.

# Interacao covariaveis ----

prep <- estimateEffect(c(20) ~ rating * day, poliblogInteraction,
                       metadata = out$meta, uncertainty = "None")
plot(prep, covariate = "day", model = poliblogInteraction,
     method = "continuous", xlab = "Days", moderator = "rating",
     moderator.value = "Liberal", linecol = "blue", ylim = c(0, .12),
     printlegend = F)

plot(prep, covariate = "day", model = poliblogInteraction,
     method = "continuous", xlab = "Days", moderator = "rating",
     moderator.value = "Conservative", linecol = "red", add = T,
     printlegend = F)
legend(0, .08, c("Liberal", "Conservative"), lwd = 2, col = c("blue", "red"))

Tópico: Também podemos traçar a influência de uma covariável no conteúdo de um tópico. Uma variável de conteúdo temático permite que o vocabulário usado para falar sobre um determinado tópico varie. Abaixo, as diferenças de vocabulário por rating são plotadas para o tópico 11. O tópico 11 está relacionado a Guantánamo.

Suas principais palavras FREX foram “tortur, detaine, court, justic, interrog, prosecut, legal”. A figura nos mostra como liberais e conservadores falam sobre esse tópico de maneira diferente. Em particular, os liberais enfatizaram a “tortur”, enquanto os conservadores enfatizam a linguagem típica dos tribunais, como “illegal” e “law”.

# Topico/covariavel: ----
plot(poliblogContent, type = "perspectives", topics = 11)

Essa função também pode ser usada para plotar o contraste de palavras em dois tópicos.

# Topico/contrastes: ----
plot(poliblogPrevFit, type = "perspectives", topics = c(12, 20))

9.2.2 Atividade Prática

Com a base de dados dos pronunciamentos registrados no dia da aprovação do processo de impeachment da Presidenta Dilma, utilize o LDA e classifique o conteúdo proferido em 2 categorias. Diante desse $k$ de categorias, é possível tirar alguma conclusão do acervo?

Referências

Atteveldt, Wouter van, Tamir Sheafer, Shaul R. Shenhav, and Yair Fogel-Dror. 2017. “Clause Analysis: Using Syntactic Information to Automatically Extract Source, Subject, and Predicate from Texts with an Application to the 2008–2009 Gaza War.” Political Analysis 25 (2): 207–22. https://doi.org/10.1017/pan.2016.12.

Bishop, Chris, C. M. Bishop, and Christopher M. Bishop. 1996. Neural Networks for Pattern Recognition. Illustrated edição. Oxford : New York: Oxford University Press, USA.

Blei, David M. 2013. “Topic Modeling and Digital Humanities.” Journal of Digital Humanities. http://journalofdigitalhumanities.org/2-1/topic-modeling-and-digital-humanities-by-david-m-blei/.

Blei, DM, and MI Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3: 993–1022.

Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.

Grimmer, Justin. 2010. “A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases.” Political Analysis 18 (1): 1–35. https://doi.org/10.1093/pan/mpp034.

Grimmer, Justin, and Brandon M. Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis, January, mps028. https://doi.org/10.1093/pan/mps028.

Hopkins, Daniel, and Gary King. 2010. “A Method of Automated Nonparametric Content Analysis for Social Science.” American Journal of Political Science 54 (1): 229–47.

Izumi, Mauricio Yoshida, and D. C. Moreira. 2018. “O Texto Como Dado: Desafios e Oportunidades Para as Ciências Sociais.” REVISTA BRASILEIRA DE INFORMAÇÃO BIBLIOGRÁFICA EM CIÊNCIAS SOCIAIS - BIB 2 (86): 138–74.

Krippendorff, Klaus. 2004. Content Analysis: An Introduction to Its Methodology. SAGE.

Moreira, Davi. 2020. “Com a Palavra Os Nobres Deputados: Ênfase Temática Dos Discursos Dos Parlamentares Brasileiros.” Dados 63 (1). https://doi.org/10.1590/001152582020204.

Moreira, Davi, Antonio Pires, and Marcelo de Almeida Medeiros. 2022. “Do ‘Texto Como Texto’ Ao ‘Texto Como Dado’: O Potencial Das Pesquisas Em Relações Internacionais.” Revista de Sociologia e Política 30 (September). https://doi.org/10.1590/1678-98732230e005.

Neuendorf, Kimberly A. 2002. The Content Analysis Guidebook. SAGE.

Quinn, Kevin M., Burt L. Monroe, Michael Colaresi, Michael H. Crespin, and Dragomir R. Radev. 2010. “How to Analyze Political Attention with Minimal Assumptions and Costs.” American Journal of Political Science 54 (1): 209–28. https://doi.org/10.1111/j.1540-5907.2009.00427.x.

Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with S. 4th edition. New York: Springer.

Vieira, Renata, and Marlo Souza. 2011. “Construction of a Portuguese Opinion Lexicon from Multiple Resources.” https://repositorio.pucrs.br/dspace/handle/10923/14064.

Ying, Luwei, Jacob M. Montgomery, and Brandon M. Stewart. 2022. “Topics, Concepts, and Measurement: A Crowdsourced Procedure for Validating Topics as Measures.” Political Analysis 30 (4): 570–89. https://doi.org/10.1017/pan.2021.33.

Young, Lori, and Stuart Soroka. 2012. “Affective News: The Automated Coding of Sentiment in Political Texts.” Political Communication 29 (2): 205–31. https://doi.org/10.1080/10584609.2012.671234.