def conditional_freq_distrubution():
cfd = nltk.ConditionalFreqDist((target, fileid[:10])
for fileid in nps.fileids()
for posts in nps.words(fileid)
for target in ['sexy', 'guy']
if posts.lower().startswith(target))
cfd.plot()
def find_all():
from nltk.corpus import gutenberg, nps_chat
"""搜索已分词文本"""
moby = nltk.Text(gutenberg.words('melville-moby_dick.txt'))
moby.findall(r"<a> (<.*>) <man>")
chat = nltk.Text(nps_chat.words())
chat.findall(r"<.*> <.*> <bro>")
chat.findall(r"<l.*>{3,}")
def searchText():
moby = nltk.Text(gutenberg.words('melville-moby_dick.txt'))
moby.findall(r"<a> (<.*>) <man>")
chat = nltk.Text(nps_chat.words())
chat.findall(r"<.*> <.*> <bro>")
chat.findall(r"<l.*>{3,}")
hobbies_learned = nltk.Text(brown.words(categories=['hobbies', 'learned']))
hobbies_learned.findall(r"<\w*> <and> <other> <\w*s>")
def searchText():
moby = nltk.Text(gutenberg.words('melville-moby_dick.txt'))
moby.findall(r"<a> (<.*>) <man>")
chat = nltk.Text(nps_chat.words())
chat.findall(r"<.*> <.*> <bro>")
chat.findall(r"<l.*>{3,}")
hobbies_learned = nltk.Text(brown.words(categories=['hobbies', 'learned']))
hobbies_learned.findall(r"<\w*> <and> <other> <\w*s>")
def searchTokenText():
from nltk.corpus import gutenberg, nps_chat
moby = nltk.Text(gutenberg.words('melville-moby_dick.txt'))
print moby.findall(r"<a> (<.*>) <man>")
chat = nltk.Text(nps_chat.words())
print chat.findall(r"<.*> <.*> <bro>")
print chat.findall(r"<l.*>{3,}")
from nltk.corpus import brown
hobbies_learned = nltk.Text(brown.words(categories=['hobbies', 'learned']))
hobbies_learned.findall(r"<\w*> <and> <other> <\w*s>")
def generate_greeting_classifier_nps():
global greeting_classifier
try:
with open('greet_classifier.pickle', 'rb') as f:
greeting_classifier = pickle.load(f)
except FileNotFoundError:
v = set([w.lower() for w in nps_chat.words()])
posts = nps_chat.xml_posts()[:5000]
h = [
(sentence_features(s.text.lower(), v=v),
s.get('class') if s.get('class') in ['Greet', 'Bye'] else 'Other')
for s in posts
]
generate_greeting_classifier(h)
with open('greet_classifier.pickle', 'wb') as f:
pickle.dump(greeting_classifier, f)
def lookupTagger(r, c): # r = range, c = corpus
if (c == "brown"):
fDist = ConditionalFreqDist(brownTW)
freqDist = FreqDist(brown.words())
wordsR = freqDist.most_common(r)
likely_tags = dict((word, fDist[word].max()) for (word, _) in wordsR)
baseline_tagger = UnigramTagger(model=likely_tags,
backoff=nltk.DefaultTagger("NN"))
return baseline_tagger
if (c == "chat"):
fDist = ConditionalFreqDist(chatTW)
freqDist = FreqDist(chat.words())
wordsR = freqDist.most_common(r)
likely_tags = dict((word, fDist[word].max()) for (word, _) in wordsR)
baseline_tagger = UnigramTagger(model=likely_tags,
backoff=nltk.DefaultTagger("NN"))
return baseline_tagger
def calculate_flags():
flagNumber = 0
tokens = nltk.word_tokenize(flagList)
# TODO: using a list of flags to be determined,
# iterate through posts to find instances of any flags
cfd = nltk.ConditionalFreqDist((tokens, fileid[:10])
for fileid in nps.fileids()
for posts in nps.words(fileid)
for target in [tokens]
#you need a check if len(samples) < 1
#you don't need to use a format specifier to get string length
if posts.lower().startswith(str(target)))
print("printing flagList " + str(tokens))
print("cfd values: " + str(cfd.keys()))
#problem here with "max() arg is an empty sequence" if we try to .tabulate()
cfd.tabulate(cumulative = True)
#!/usr/bin/python3
# coding: utf-8
from nltk.corpus import nps_chat
##################################################################
## 简单查看
print(type(
nps_chat)) # <class 'nltk.corpus.reader.nps_chat.NPSChatCorpusReader'>
print(len(nps_chat.fileids())) # 15
print(
nps_chat.fileids()
) # ['10-19-20s_706posts.xml', '10-19-30s_705posts.xml', '10-19-40s_686posts.xml', '10-19-adults_706posts.xml', '10-24-40s_706posts.xml', '10-26-teens_706posts.xml', '11-06-adults_706posts.xml', '11-08-20s_705posts.xml', '11-08-40s_706posts.xml', '11-08-adults_705posts.xml', '11-08-teens_706posts.xml', '11-09-20s_706posts.xml', '11-09-40s_706posts.xml', '11-09-adults_706posts.xml', '11-09-teens_706posts.xml']
print(len(nps_chat.words('10-19-20s_706posts.xml'))) # 2829
print(
nps_chat.words('10-19-20s_706posts.xml')[:10]
) # ['now', 'im', 'left', 'with', 'this', 'gay', 'name', ':P', 'PART', 'hey']
##################################################################
## posts()
chatroom = nps_chat.posts('10-19-20s_706posts.xml')
print(
chatroom[123]
) # ['i', 'do', "n't", 'want', 'hot', 'pics', 'of', 'a', 'female', ',', 'I', 'can', 'look', 'in', 'a', 'mirror', '.']
# Write a function novel10(text) that prints any word that appeared in the last 10% of a text that had not been encountered earlier.
from nltk.corpus import nps_chat
def novel10(text):
# finds the point at which to cut the text
cut = int(0.9 * len(text))
#cuts the text
first_part, second_part = text[:cut], text[cut:]
# makes a set of each part, leaving only unique words
unique_words_first_part = set(first_part)
unique_words_second_part = set(second_part)
# makes a new list of words that only appear in the last 10%
return [
word for word in unique_words_second_part
if word not in unique_words_first_part
]
text = nps_chat.words()
print(novel10(text))
import re
from random import shuffle
from nltk.corpus import webtext
from nltk.corpus import nps_chat
from gensim.models.doc2vec import LabeledSentence, Doc2Vec
gendered_terms = [
r'\bhe\b', r'\bhes', r'\bshe\b', r'\bshes\b', r'\bhis\b', r'\bher\b',
r'\bbro\b', r'\bman\b', r'\bsir\b', r'\bdude\b', r'\bgirl\b', r'\bgirls\b',
r'\blady\b', r'\bgurl\b', r'\bhims\b', r'\bhers\b', r'\bhisself\b',
r'\bherself\b', r'\bman\b', r'\bwoman\b'
]
dictionary_words = {}
for x in nps_chat.words() + webtext.words():
dictionary_words[x] = True
print(len(dictionary_words))
class LabeledLineSentence(object):
def __init__(self, messages_dic, is_sample=True):
self.documents = []
self.messages_dic = messages_dic
self.is_sample = is_sample
def __iter__(self):
for user in self.messages_dic:
if self.is_sample:
for i in range(200):
import os, sys, re
from nltk.corpus import brown
from nltk.corpus import cess_cat
from nltk.corpus import nps_chat
from nltk.probability import LidstoneProbDist
from nltk.model import NgramModel
from nltk.tokenize import word_tokenize, wordpunct_tokenize # Tokenizer
from nltk.tokenize import RegexpTokenizer
if __name__ == "__main__":
urlRegex = '(http|ftp|https):\/\/[\w\-_]+(\.[\w\-_]+)+([\w\-\.,@?^=%&:/~\+#]*[\w\-\@?^=%&/~\+#])?'
specRegex = "([#@]+[\w']+)"
symbolsRegex = '[\^=<>.,!?:;\(\)_\"]+'
simpleWordRegex = "[\w'-]+"
tTwit = list(nps_chat.words())
# estimator for smoothing the N-gram model
estimator1 = lambda fdist, bins: LidstoneProbDist(fdist, 0.2)
tokens1 = list(brown.words())
# N-gram language model with 3-grams
model = NgramModel(3, tokens1, estimator=estimator1)
twitsFile = sys.argv[1]
varsFile = sys.argv[2]
outFile = sys.argv[3]
outTwitFile = sys.argv[4]
mode = sys.argv[5]
import nltk
import time
nltk.download('nps_chat')
from nltk.corpus import nps_chat
for i in nps_chat.words():
print("Raw word: " + i)
token = nltk.word_tokenize(i)[0]
print("Token: " + token)
print("---")
time.sleep(.5)
'English: Brown Corpus':
lambda: brown.words(),
'English: Brown Corpus (Press)':
lambda: brown.words(categories=['news', 'editorial', 'reviews']),
'English: Brown Corpus (Religion)':
lambda: brown.words(categories='religion'),
'English: Brown Corpus (Learned)':
lambda: brown.words(categories='learned'),
'English: Brown Corpus (Science Fiction)':
lambda: brown.words(categories='science_fiction'),
'English: Brown Corpus (Romance)':
lambda: brown.words(categories='romance'),
'English: Brown Corpus (Humor)':
lambda: brown.words(categories='humor'),
'English: NPS Chat Corpus':
lambda: nps_chat.words(),
'English: Wall Street Journal Corpus':
lambda: treebank.words(),
'Chinese: Sinica Corpus':
lambda: sinica_treebank.words(),
'Dutch: Alpino Corpus':
lambda: alpino.words(),
'Hindi: Indian Languages Corpus':
lambda: indian.words(files='hindi.pos'),
'Portuguese: Floresta Corpus (Portugal)':
lambda: floresta.words(),
'Portuguese: MAC-MORPHO Corpus (Brazil)':
lambda: mac_morpho.words(),
'Portuguese: Machado Corpus (Brazil)':
lambda: machado.words(),
'Spanish: CESS-ESP Corpus':
cv_word_pairs = [(cv,w) for w in rotokas_words
for cv in re.findall(r'[ptksvr][aeiou]',w)]
cv_index = nltk.Index(cv_word_pairs)
print(cv_index['su'])
print(cv_index['po'])
import re
import nltk
def stem(word):
for suffix in ['ing','ly','ed','ious','ies','ive','es','s','ment']:
if word.endswith(suffix):
return word[:-len(suffix)]
return word
print(re.findall(r'^.*(ing|ly|ed|ious|ies|ive|es|s|ment)$','processing'))
def stem(word):
regexp = r'^(.*?)(ing|ly|ed|ious|ies|ive|es|s|ment)?$'
stem,suffix = re.findall(regexp,word)[0]
return stem
from nltk.corpus import gutenberg,nps_chat
import nltk
moby = nltk.Text(gutenberg.words('melville-moby_dick.txt'))
print(moby.findall(r"<a> (<.*>) <man>"))
chat = nltk.Text(nps_chat.words())
print(chat.findall(r"<.*><.*><bro>"))
print(chat.findall(r"<1.*>{3,}"))
def classify_greeting(s):
v = set([w.lower() for w in nps_chat.words()])
return greeting_classifier.classify(sentence_features(s.lower(), v=v))
from nltk.corpus import nps_chat
from nltk.corpus import brown
from nltk.corpus import names
from normalise.data.contraction_list import contractions
from normalise.data.tech_words import tech_words
mod_path = os.path.dirname(__file__)
with open('{}/data/wordlist.pickle'.format(mod_path), mode='rb') as file:
wordlist = pickle.load(file)
with open('{}/data/fake_data.pickle'.format(mod_path), mode='rb') as file:
fake_data = pickle.load(file)
if __name__ == '__main__':
word_tokenized = brown.words() + nps_chat.words() + fake_data
brown_lower = {w.lower() for w in brown.words()
if len(w) > 4 and w.isalpha()}
names_lower = {w.lower() for w in names.words()}
words_lower = {w.lower() for w in words.words('en') if len(w) > 1}
wordlist = brown_lower | names_lower | words_lower | set(tech_words) | {'I', 'i', 'a', 'A'}
word_tokenized_lowered = [w.lower() if w.lower() in wordlist
else w for w in word_tokenized]
word_tokenized = list(word_tokenized)
# Conditions for identification of NSWs.
def cond1(w):
""" Return word if its lower-cased form is not in the wordlist."""
return w.lower() not in wordlist or w == 'US'
R0=random.getstate()[1][(long(test_seed%10000))%625]
random.shuffle(news_words, lambda: 1/R0) # deterministic shuffling using seeds
test_seed/=10000
filtered_news_words = list(set(news_words)-stop)
[related_words_NEWS.append(i) for i in filtered_news_words if len(i)>3]
related_words_NEWS = related_words_NEWS[0:2000]
print "finished extracted news data...\n"
#------------------------------------------------------------
#--------------------------- CHAT ---------------------------
print "starting extracted chat data...\n"
if CHAT_FLAG != 'n':
chat_words = list(chat.words())
R1=random.getstate()[1][(long(test_seed%10000))%625]
random.shuffle(chat_words, lambda: 1/R1) # deterministic shuffling using seeds
test_seed/=10000
filtered_chat_words = list(set(chat_words)-stop)
[related_words_CHAT.append(i) for i in filtered_chat_words]
related_words_CHAT = related_words_CHAT[0:2000]
print "finished extracted chat data...\n"
#------------------------------------------------------------
#if BOOK_FLAG != 'n':
# book_words = gut.words(gut.fileids()[int(shakespeare_books.get(str(FAV_BOOK)))])
CORPUS_LOADED_EVENT = "<<CL_EVENT>>"
ERROR_LOADING_CORPUS_EVENT = "<<ELC_EVENT>>"
POLL_INTERVAL = 100
_DEFAULT = "English: Brown Corpus (Humor)"
_CORPORA = {
"Catalan: CESS-CAT Corpus": lambda: cess_cat.words(),
"English: Brown Corpus": lambda: brown.words(),
"English: Brown Corpus (Press)": lambda: brown.words(categories=["news", "editorial", "reviews"]),
"English: Brown Corpus (Religion)": lambda: brown.words(categories="religion"),
"English: Brown Corpus (Learned)": lambda: brown.words(categories="learned"),
"English: Brown Corpus (Science Fiction)": lambda: brown.words(categories="science_fiction"),
"English: Brown Corpus (Romance)": lambda: brown.words(categories="romance"),
"English: Brown Corpus (Humor)": lambda: brown.words(categories="humor"),
"English: NPS Chat Corpus": lambda: nps_chat.words(),
"English: Wall Street Journal Corpus": lambda: treebank.words(),
"Chinese: Sinica Corpus": lambda: sinica_treebank.words(),
"Dutch: Alpino Corpus": lambda: alpino.words(),
"Hindi: Indian Languages Corpus": lambda: indian.words(files="hindi.pos"),
"Portuguese: Floresta Corpus (Portugal)": lambda: floresta.words(),
"Portuguese: MAC-MORPHO Corpus (Brazil)": lambda: mac_morpho.words(),
"Portuguese: Machado Corpus (Brazil)": lambda: machado.words(),
"Spanish: CESS-ESP Corpus": lambda: cess_esp.words(),
}
class CollocationsView:
_BACKGROUND_COLOUR = "#FFF" # white
def __init__(self):
#using regex
wordlist_suffixes = [
suffix for w in wordlist
for suffix in re.findall(r'^.*(ing|ly|ed|ious|ies|ive|es|s|ment)$', w)
]
print(nltk.FreqDist(wordlist_suffixes).most_common(20))
raw = """DENNIS: Listen, strange women lying in ponds distributing swords is no basis for a system of government. Supreme executive power derives from a mandate from the masses, not from some farcical aquatic ceremony."""
raw_tokens = word_tokenize(raw)
raw_stems = [stem(t) for t in raw_tokens]
print(raw_stems)
#searching tokenized text
moby = nltk.Text(gutenberg.words('melville-moby_dick.txt'))
print(moby.findall(r'<a><man>')) #print only a man
print(moby.findall(r'<a>(<.*>)<man>')) #prints words between a and man
chat_words = nltk.Text(nps_chat.words())
print(chat_words.findall(r'<.*><.*><bro>'))
print(chat_words.findall(r'<1.*>{3,}'))
#discover hypernyms in text i.e a and other ys
hobbies_learned = nltk.Text(brown.words(categories=['hobbies', 'learned']))
print(hobbies_learned.findall(r'<\w*><and><other><\w*s>'))
print(hobbies_learned.findall(r'<\w*><as><\w*>'))
#text normalization
#stemmers - to remove affixes from words, 2 off-the-shelf in nltk 1.PorterStemmer 2.LancasterStemmer
print(raw_tokens)
porter = nltk.PorterStemmer()
lancaster = nltk.LancasterStemmer()
print([porter.stem(w) for w in raw_tokens])
print([lancaster.stem(w) for w in raw_tokens])
categorized_sentences = pickle.load(f)
f.close()
except FileNotFoundError:
categorized_sentences = []
# load up categorized sentences if found
try:
f = open('sentence_clusters.pickle', 'rb')
sentence_clusters= pickle.load(f)
f.close()
except FileNotFoundError:
sentence_clusters = []
# preprocessing nps chat corpus for sentence classification
all_words = nltk.FreqDist(w.lower() for w in nps_chat.words())
word_features = [a[0] for a in all_words.most_common()[:2000]]
sentences = [(nltk.word_tokenize(a.text.lower()), a.attrib['class']) for a in nps_chat.xml_posts()]
# logical response types for each input sentence type
response_types = {
'Accept': ['Statement', 'Emotion', 'Emphasis'],
'Bye': ['Bye'],
'Clarify': ['Accept', 'Reject', 'Statement', 'Emphasis'],
'Emotion': ['Accept', 'Reject', 'Statement', 'Emotion', 'Emphasis'],
'Continuer': ['Accept', 'Reject', 'Statement', 'Emphasis'],
'Emphasis': ['Accept', 'Reject', 'Statement', 'Emotion', 'Emphasis'],
'Greet': ['Greet'],
'Other': ['Statement'],
'Reject': ['Statement', 'Emotion', 'Emphasis'],
'Statement': ['Accept', 'Reject', 'Statement', 'Emotion', 'Emphasis'],
# Write a function novel10(text) that prints any word that appeared in the last 10% of a text that had not been encountered earlier. from nltk.corpus import nps_chat def novel10(text): # finds the point at which to cut the text cut = int(0.9 * len(text)) #cuts the text first_part, second_part = text[:cut], text[cut:] # makes a set of each part, leaving only unique words unique_words_first_part = set(first_part) unique_words_second_part = set(second_part) # makes a new list of words that only appear in the last 10% return [word for word in unique_words_second_part if word not in unique_words_first_part] text = nps_chat.words() print(novel10(text))
_DEFAULT = "English: Brown Corpus (Humor)"
_CORPORA = {
"Catalan: CESS-CAT Corpus": lambda: cess_cat.words(),
"English: Brown Corpus": lambda: brown.words(),
"English: Brown Corpus (Press)": lambda: brown.words(
categories=["news", "editorial", "reviews"]
),
"English: Brown Corpus (Religion)": lambda: brown.words(categories="religion"),
"English: Brown Corpus (Learned)": lambda: brown.words(categories="learned"),
"English: Brown Corpus (Science Fiction)": lambda: brown.words(
categories="science_fiction"
),
"English: Brown Corpus (Romance)": lambda: brown.words(categories="romance"),
"English: Brown Corpus (Humor)": lambda: brown.words(categories="humor"),
"English: NPS Chat Corpus": lambda: nps_chat.words(),
"English: Wall Street Journal Corpus": lambda: treebank.words(),
"Chinese: Sinica Corpus": lambda: sinica_treebank.words(),
"Dutch: Alpino Corpus": lambda: alpino.words(),
"Hindi: Indian Languages Corpus": lambda: indian.words(files="hindi.pos"),
"Portuguese: Floresta Corpus (Portugal)": lambda: floresta.words(),
"Portuguese: MAC-MORPHO Corpus (Brazil)": lambda: mac_morpho.words(),
"Portuguese: Machado Corpus (Brazil)": lambda: machado.words(),
"Spanish: CESS-ESP Corpus": lambda: cess_esp.words(),
}
class CollocationsView:
_BACKGROUND_COLOUR = "#FFF" # white
def __init__(self):
from nltk.corpus import nps_chat
from nltk import FreqDist
first_n = 100
a_fq = FreqDist(nps_chat.words('11-09-adults_706posts.xml'))
t_fq = FreqDist(nps_chat.words('11-09-teens_706posts.xml'))
a_words = set(a_fq.keys()[:first_n])
t_words = set(t_fq.keys()[:first_n])
print "common used words:"
print ','.join( a_words.intersection(t_words) )
print
print "adult use while teens not use:"
print ','.join( a_words - t_words )
print
print "teen use while adult not use:"
print ','.join( t_words - a_words )
from nltk.corpus import gutenberg, nps_chat
import nltk
moby = nltk.Text(gutenberg.words("melville-moby_dick.txt"))
#findall - text class - regular expression
print(moby.findall(r'<a><.*><man>'))
chat_obj = nltk.Text(nps_chat.words())
print(chat_obj.findall(r'<.*><.*><bro>'))
print(chat_obj.findall(r'<a><.*><man>'))
text = "Hello, I am a computer programmer who is currently learning and studying NLP"
our_own_text_obj = nltk.Text(nltk.word_tokenize(text))
print(our_own_text_obj.findall(r'<.*ing>+'))
regexp = r'^(.*?)(ing|ly|ed|ious|ies|ive|es|s|ment)?$'
stem, suffix = re.findall(regexp, word)[0]
return stem
raw = """DENNIS: Listen, strange woman lying in ponds distributing swords
is no basis for a system of government. Supreme executive power derives from
a mandate from the masses, not from some farcical aquatic ceremony."""
tokens = word_tokenize(raw)
print([stem(t) for t in tokens])
print("-" * 40)
print("Searching Tokenized Text")
from nltk.corpus import gutenberg, nps_chat
moby = nltk.Text(gutenberg.words('melville-moby_dick.txt'))
moby.findall(r'<a>(<.*>)<man>')
chat = nltk.Text(nps_chat.words())
chat.findall(r'<.*> <.*> <bro>')
chat.findall(r'<l.*>{3,}')
print("-" * 40)
nltk.re_show('kaa', ' '.join(rotokas_words))
nltk.app.nemo()
print("-" * 40)
from nltk.corpus import brown
hobbies_learned = nltk.Text(brown.words(categories=['hobbies', 'learned']))
hobbies_learned.findall(r'<\w*> <and> <other> <\w*s>')
print("-" * 40)
hobbies_learned.findall(r'<as> <\w*> <as> <\w*>')
print("-" * 40)
import nltk
from nltk.tokenize import TweetTokenizer
from nltk.corpus import nps_chat as nps
import os
# twitterSamples = nltk.corpus.twitter_samples
# negTweets = twitter_samples.strings('negative_tweets.json')
teenChat = nps.xml_posts("11-08-teens_706posts.xml")
chatWords = nps.words("11-08-teens_706posts.xml")
chatBigrams = nltk.bigrams(chatWords)
cfd = nltk.ConditionalFreqDist(chatBigrams)
maxConfidence = 100
flagFile = open('flagList.txt')
flagList = flagFile.read()
def calculate_flags():
flagNumber = 0
tokens = nltk.word_tokenize(flagList)
# TODO: using a list of flags to be determined,
# iterate through posts to find instances of any flags
cfd = nltk.ConditionalFreqDist((tokens, fileid[:10])
for fileid in nps.fileids()
for posts in nps.words(fileid)
for target in [tokens]
#you need a check if len(samples) < 1
print("Type the name of the text or sentence to view it.")
print("Type: 'texts()' or 'sents()' to list the materials.")
text1 = Text(gutenberg.words('melville-moby_dick.txt'))
print("text1:", text1.name)
text2 = Text(gutenberg.words('austen-sense.txt'))
print("text2:", text2.name)
text3 = Text([str(w) for w in genesis.words('english-kjv.txt')], name="The Book of Genesis")
print("text3:", text3.name)
text4 = Text(inaugural.words(), name="Inaugural Address Corpus")
print("text4:", text4.name)
text5 = Text(nps_chat.words(), name="Chat Corpus")
print("text5:", text5.name)
text6 = Text(webtext.words('grail.txt'), name="Monty Python and the Holy Grail")
print("text6:", text6.name)
text7 = Text(treebank.words(), name="Wall Street Journal")
print("text7:", text7.name)
text8 = Text(webtext.words('singles.txt'), name="Personals Corpus")
print("text8:", text8.name)
text9 = Text(gutenberg.words('chesterton-thursday.txt'))
print("text9:", text9.name)
def texts():
cfd = nltk.ConditionalFreqDist(bigrams)
print(cfd['living'])
genrate_model(cfd, 'living')
#lexical resources - wordlist with info such as lexical resources, sense definition etc.
#unusual words
def unusual_words(text):
text_vocab = set(w.lower() for w in text if w.isalpha())
english_vocab = set(w.lower() for w in nltk.corpus.words.words())
unusual = text_vocab - english_vocab
return sorted(unusual)
unusual_words(gutenberg.words('austen-sense.txt'))
unusual_words(nps_chat.words())
#stop words such as if the for etc.
print(stopwords.words('english'))
#function to compute what % of words are not is stopwords list
def content_fraction(text):
stopwords_list = stopwords.words('english')
content = [w for w in text if w.lower() not in stopwords_list]
return len(content) / len(text) * 100
content_fraction(reuters.words())
#solving word puzzle
puzzle_letters = nltk.FreqDist('egivrvonl')
obligatory = 'r'
'English: Brown Corpus': lambda: brown.words(), 'English: Brown Corpus (Press)': lambda: brown.words(categories=['news', 'editorial', 'reviews']), 'English: Brown Corpus (Religion)': lambda: brown.words(categories='religion'), 'English: Brown Corpus (Learned)': lambda: brown.words(categories='learned'), 'English: Brown Corpus (Science Fiction)': lambda: brown.words(categories='science_fiction'), 'English: Brown Corpus (Romance)': lambda: brown.words(categories='romance'), 'English: Brown Corpus (Humor)': lambda: brown.words(categories='humor'), 'English: NPS Chat Corpus': lambda: nps_chat.words(), 'English: Wall Street Journal Corpus': lambda: treebank.words(), 'Chinese: Sinica Corpus': lambda: sinica_treebank.words(), 'Dutch: Alpino Corpus': lambda: alpino.words(), 'Hindi: Indian Languages Corpus': lambda: indian.words(files='hindi.pos'), 'Portuguese: Floresta Corpus (Portugal)': lambda: floresta.words(), 'Portuguese: MAC-MORPHO Corpus (Brazil)': lambda: mac_morpho.words(), 'Portuguese: Machado Corpus (Brazil)': lambda: machado.words(), 'Spanish: CESS-ESP Corpus':
#These libraries are for cleaning text data
#re is the Python library for Regular Expressions
import re
#nltk is the python library for Natural Language Processing (used here for cleaning non-English text from the data)
from nltk.corpus import brown
from nltk.corpus import words
from nltk.corpus import cess_esp as spanish
from nltk.corpus import reuters
from nltk.corpus import nps_chat
#These dictionaries are used to reduce time required to search for English words by implementing a hash search in "isEnglishWord"
englishBrownDict = dict.fromkeys(brown.words(), True)
englishWordsDict = dict.fromkeys(words.words(), True)
englishReutersDict = dict.fromkeys(reuters.words(), True)
englishChatDict = dict.fromkeys(nps_chat.words(), True)
spanishWordsDict = dict.fromkeys(spanish.words(), True)
malayText = open(os.path.join(os.getcwd(), "malayUpdated.txt"))
malayWordsDict = []
for line in malayText:
malayWordsDict.append(line)
#print "Count of malay words: ", len (malayWords), "\n"
#malayWordsDict = dict.fromkeys (malayWords, True)
commonTweetWords = [
"ur", "u", "youre", "gonna", "wanna", "wannabe", "shoulda", "should've",
print("Type the name of the text or sentence to view it.")
print("Type: 'texts()' or 'sents()' to list the materials.")
text1 = Text(gutenberg.words("melville-moby_dick.txt"))
print("text1:", text1.name)
text2 = Text(gutenberg.words("austen-sense.txt"))
print("text2:", text2.name)
text3 = Text(genesis.words("english-kjv.txt"), name="The Book of Genesis")
print("text3:", text3.name)
text4 = Text(inaugural.words(), name="Inaugural Address Corpus")
print("text4:", text4.name)
text5 = Text(nps_chat.words(), name="Chat Corpus")
print("text5:", text5.name)
text6 = Text(webtext.words("grail.txt"), name="Monty Python and the Holy Grail")
print("text6:", text6.name)
text7 = Text(treebank.words(), name="Wall Street Journal")
print("text7:", text7.name)
text8 = Text(webtext.words("singles.txt"), name="Personals Corpus")
print("text8:", text8.name)
text9 = Text(gutenberg.words("chesterton-thursday.txt"))
print("text9:", text9.name)
# global list of gold corpora
# C:\Users\admin\AppData\Roaming\nltk_data\corpora\
corp_names = [
"brown", "nps_chat", "conll2000", "treebank", "twitter", "nhtsa_0",
"nhtsa_1", "nhtsa_2", "nhtsa_3", "nhtsa_4", "nhtsa_5", "nhtsa_6"
]
corp_words_tagged = [
brown.tagged_words(tagset=CONST_tagset),
nps_chat.tagged_words(tagset=CONST_tagset),
conll2000.tagged_words(tagset=CONST_tagset),
treebank.tagged_words(tagset=CONST_tagset)
]
corp_words_untagged = [
brown.words(),
nps_chat.words(),
conll2000.words(),
treebank.words()
]
corp_sents_tagged = [
brown.tagged_sents(tagset=CONST_tagset),
nps_chat.tagged_posts(tagset=CONST_tagset),
conll2000.tagged_sents(tagset=CONST_tagset),
treebank.tagged_sents(tagset=CONST_tagset)
]
corp_sents_untagged = [
brown.sents(),
nps_chat.posts(),
conll2000.sents(),
treebank.sents()
]
