def extractWordPieces():
word = 'supercalifragilisticexpialidocious'
re.findall(r'[aeiou]', word)
len(re.findall(r'[aeiou]', word))
wsj = sorted(set(nltk.corpus.treebank.words()))
fd = nltk.FreqDist(vs for word in wsj
for vs in re.findall(r'[aeiou]{2,}', word))
fd.items()
regexp = r'^[AEIOUaeiou]+|[AEIOUaeiou]+$|[^AEIOUaeiou]'
def compress(word):
pieces = re.findall(regexp, word)
return ''.join(pieces)
english_udhr = nltk.corpus.udhr.words('English-Latin1')
print nltk.tokenwrap(compress(w) for w in english_udhr[:75])
rotokas_words = nltk.corpus.toolbox.words('rotokas.dic')
cvs = [cv for w in rotokas_words for cv in re.findall(r'[ptksvr][aeiou]', w)]
cfd = nltk.ConditionalFreqDist(cvs)
cfd.tabulate()
cv_word_pairs = [(cv, w) for w in rotokas_words
for cv in re.findall(r'[ptksvr][aeiou]', w)]
cv_index = nltk.Index(cv_word_pairs)
cv_index['su']
cv_index['po']
def extractWordPieces():
word = 'supercalifragilisticexpialidocious'
re.findall(r'[aeiou]', word)
len(re.findall(r'[aeiou]', word))
wsj = sorted(set(nltk.corpus.treebank.words()))
fd = nltk.FreqDist(vs for word in wsj
for vs in re.findall(r'[aeiou]{2,}', word))
fd.items()
regexp = r'^[AEIOUaeiou]+|[AEIOUaeiou]+$|[^AEIOUaeiou]'
def compress(word):
pieces = re.findall(regexp, word)
return ''.join(pieces)
english_udhr = nltk.corpus.udhr.words('English-Latin1')
print nltk.tokenwrap(compress(w) for w in english_udhr[:75])
rotokas_words = nltk.corpus.toolbox.words('rotokas.dic')
cvs = [
cv for w in rotokas_words for cv in re.findall(r'[ptksvr][aeiou]', w)
]
cfd = nltk.ConditionalFreqDist(cvs)
cfd.tabulate()
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)
cv_index['su']
cv_index['po']
def Analysis(corpus):
raw = open(corpus, "rU").read()
words, sents, text = Tokenize(raw)
random_sentence = randomSentence(sents)
fdist, cfd, top_words = FreqDists(words)
print nltk.tokenwrap(Przm(w) for w in words[:20])
print fdist
cfd.tabulate()
def regex_compress():
def compress(word):
pieces = re.findall(regexp, word)
return ''.join(pieces)
english_udhr = nltk.corpus.udhr.words('English-Latin1')
regexp = r'^[AEIOUaeiou]+|[AEIOUaeiou]+$|[^AEIOUaeiou]'
return nltk.tokenwrap(compress(w) for w in english_udhr[:75])
def generate(self, length=100):
""""""
# Change tokens
self.tokens = nltk.word_tokenize(
self.__words[randint(1, len(self.__words)) - 1])
estimator = lambda fdist, bins: nltk.LidstoneProbDist(
fdist, self.__random.random())
#estimator = lambda fdist, bins: nltk.LidstoneProbDist(fdist, 0.2)
self._trigram_model = nltk.NgramModel(self.__random.randint(3, 15),
self, estimator)
#self._trigram_model = nltk.NgramModel(3, self, estimator)
text = self._trigram_model.generate(length)
return nltk.tokenwrap(text)
nacute_utf = nacute.encode('utf8')
print repr(nacute_utf)
#正则表达式
import re
wordlist = [w for w in nltk.corpus.words.words('en') if w.islower()]
[w for w in wordlist if re.search('ed$', w)]
[w for w in wordlist if re.search('^..j..t..$', w)]
[w for w in wordlist if re.search('^[ghi][mno][jlk][def]$', w)]
regexp = r'^[AEIOUaeiou]+|[AEIOUaeiou]+$|[^AEIOUaeiou]'
def compress(word):
pieces = re.findall(regexp, word)
return ''.join(pieces)
english_udhr = nltk.corpus.udhr.words('English-Latin1')
print nltk.tokenwrap(compress(w) for w in english_udhr[:75])
#处理词干
def stem(word):
for suffix in ['ing', 'ly', 'ed', 'ious', 'ies', 'ive', 'es', 's', 'ment']:
if word.endswith(suffix):
return word[:-len(suffix)]
return word
re.findall(r'^.*(ing|ly|ed|ious|ies|ive|es|s|ment)$', 'processing')
re.findall(r'^.*(?:ing|ly|ed|ious|ies|ive|es|s|ment)$', 'processing')
re.findall(r'^(.*)(ing|ly|ed|ious|ies|ive|es|s|ment)$', 'processing')
#非贪婪
re.findall(r'^(.*?)(ing|ly|ed|ious|ies|ive|es|s|ment)$', 'processes')
def stem(word):
regexp = r'^(.*?)(ing|ly|ed|ious|ies|ive|es|s|ment)?$'
def compress_vowels():
# initial vowel sequence, final vowel sequence or consonents,
# everything else is removed
regex = r"^[AEIOUaeiou]+|[AEIOUaeiou]+$|[^AEIOUaeiou]"
english_udhr = nltk.corpus.udhr.words("English-Latin1")
print nltk.tokenwrap([compress(regex, w) for w in english_udhr[:75]])
raw.rfind('End of Project')
# regular expression
import re
wordlist = [w for w in nltk.corpus.words.words('en') if w.islower()]
[w for w in wordlist if re.search('ed$',w) and len(w)==3]
[w for w in wordlist if re.search('ed$',w) and len(w)==4]
[w for w in wordlist if re.search('^..j..t..$',w)]
[w for w in wordlist if re.search('^[abdc][efgh][ijkl]$',w)]
regexp = r'^[AEIOUaeiou]+|[AEIOUaeiou]+$|[AEIOUaeiou]'
def compress(word):
pieces = re.findall(regexp,word)
return ''.join(pieces)
english_udhr = nltk.corpus.udhr.words('English-Latin1')
print (nltk.tokenwrap(compress(w) for w in english_udhr[:100]))
# suffix, stemming
re.findall(r'^.*(ing|ly|ed|ious|ive|es|s|ment)$','processing')
re.findall(r'^.*(?:ing|ly|ed|ious|ive|es|s|ment)$','processing')
re.findall(r'^(.*)(ing|ly|ed|ious|ive|es|s|ment)$','processes')
re.findall(r'^(.*?)(ing|ly|ed|ious|ive|es|s|ment)?$','processes')
def stem(word):
regexp = r'^(.*?)(ing|ly|ed|ious|ive|es|s|ment)?$'
stem, suffix = re.findall(regexp,word)[0]
return stem
stem('processing')
# searching tokenized text
moby = nltk.Text(nltk.corpus.gutenberg.words('melville-moby_dick.txt'))
moby.findall(r"<a>(<.*>)<man>")
def testCompress():
english_udhr = nltk.corpus.udhr.words('English-Latin1')
print nltk.tokenwrap(compress(w) for w in english_udhr[:75])
def compress_vowels():
# initial vowel sequence, final vowel sequence or consonents,
# everything else is removed
regex = r"^[AEIOUaeiou]+|[AEIOUaeiou]+$|[^AEIOUaeiou]"
english_udhr = nltk.corpus.udhr.words("English-Latin1")
print nltk.tokenwrap([compress(regex, w) for w in english_udhr[:75]])
def lossy_compression(self):
pattern = '^[AEIOUaeiou]+|[AEIOUaeiou]+$|[^AEIOUaeiou]'
print nltk.tokenwrap(self.compress(pattern, w) for w in self.wordlist)