本文整理汇总了Python中nltk.RegexpTokenizer方法的典型用法代码示例。如果您正苦于以下问题:Python nltk.RegexpTokenizer方法的具体用法?Python nltk.RegexpTokenizer怎么用?Python nltk.RegexpTokenizer使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nltk
的用法示例。
在下文中一共展示了nltk.RegexpTokenizer方法的13个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: load_data
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def load_data():
global N, words
raw = list(word
for fileid in corpus.fileids()
for word in corpus.words(fileid))
words = list(token for token in RegexpTokenizer('\w+').tokenize(' '.join(raw)))[100:1000]
tokens = set(words)
tokens_l = list(tokens)
N = len(tokens)
print 'Corpus size: {} words'.format(N)
step = 4
data = []
for gram in ngrams(words, step):
w1, w2, w3, pred = gram
V = Vol(1, 1, N, 0.0)
V.w[tokens_l.index(w1)] = 1
V.w[tokens_l.index(w2)] = 1
V.w[tokens_l.index(w3)] = 1
label = tokens_l.index(pred)
data.append((V, label))
return data
示例2: test
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def test():
gt = GetTweets()
documents = gt.get_hashtag('ferguson', count=20)
documents += gt.get_hashtag('police', count=21)
print 'Query:', documents[-1]
tokenizer = RegexpTokenizer('\w+')
vols = []
for doc in documents:
samples = []
for token in tokenizer.tokenize(doc):
word = token.lower()
if word not in ENGLISH_STOP_WORDS and word not in punctuation:
samples.append(word)
vols.append(volumize(FreqDist(samples)))
vectors = [ doc_code(v) for v in vols[:-1] ]
query_vec = doc_code(vols[-1])
sims = [ cos(v, query_vec) for v in vectors ]
m = max(sims)
print m, documents[sims.index(m)]
示例3: convert_to_vw
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def convert_to_vw(text):
tokenizer = nltk.RegexpTokenizer(r'\w+')
lmtzr = WordNetLemmatizer()
tokens = [t.lower() for t in tokenizer.tokenize(text)]
id_ = 13371337
processed = []
for t in tokens:
l = lmtzr.lemmatize(t)
processed.append(l)
counted = Counter(processed)
res_str = str(id_)
for k, v in counted.items():
if v != 1:
res_str = res_str + " {}:{}".format(k, v)
else:
res_str = res_str + " {}".format(k)
return res_str
示例4: update_hashtags_stats
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def update_hashtags_stats(hashtags_fd, json_tweet):
tweet = utils.extract_tweet_from_json(json_tweet)
tweet_terms = []
if tweet is None or '#' not in tweet:
return False
tokenizer = nltk.RegexpTokenizer('\#?[\w\d]+')
doc = tokenizer.tokenize(tweet)
for w_raw in doc:
if '#' not in w_raw:
continue
w = (w_raw.strip('\"\'.,;?!:)(@/*&')).lower()
tweet_terms.append(w)
hashtags_fd.inc(w)
return True
#processes the tweet and updates terms_fd based on the tweet terms
#specifically, if the term was already encountered it adds it to the freq dict,
# otherwise it increases the term counter
示例5: getTokens
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def getTokens(self, removeStopwords=True):
""" Tokenizes the text, breaking it up into words, removing punctuation. """
tokenizer = nltk.RegexpTokenizer('[a-zA-Z]\w+\'?\w*') # A custom regex tokenizer.
spans = list(tokenizer.span_tokenize(self.text))
# Take note of how many spans there are in the text
self.length = spans[-1][-1]
tokens = tokenizer.tokenize(self.text)
tokens = [ token.lower() for token in tokens ] # make them lowercase
stemmer = LancasterStemmer()
tokens = [ stemmer.stem(token) for token in tokens ]
if not removeStopwords:
self.spans = spans
return tokens
tokenSpans = list(zip(tokens, spans)) # zip it up
stopwords = nltk.corpus.stopwords.words('english') # get stopwords
tokenSpans = [ token for token in tokenSpans if token[0] not in stopwords ] # remove stopwords from zip
self.spans = [ x[1] for x in tokenSpans ] # unzip; get spans
return [ x[0] for x in tokenSpans ] # unzip; get tokens
示例6: get_tokenize
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def get_tokenize():
return RegexpTokenizer(r'\w+|#\w+|<\w+>|%\w+|[^\w\s]+').tokenize
示例7: preprocess_data
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def preprocess_data(text):
global sentences, tokenized
tokenizer = nltk.RegexpTokenizer(r'\w+')
sentences = nltk.sent_tokenize(text)
tokenized = [tokenizer.tokenize(s) for s in sentences]
# import the data
示例8: get_tokenize
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def get_tokenize():
return nltk.RegexpTokenizer(r'\w+|#\w+|<\w+>|%\w+|[^\w\s]+').tokenize
示例9: get_chat_tokenize
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def get_chat_tokenize():
return nltk.RegexpTokenizer(u'\w+|:d|:p|<sil>|<men>|<hash>|<url>|'
u'[\U0001f600-\U0001f64f\U0001f300-\U0001f5ff\U0001f680-\U0001f6ff]|'
u'[^\w\s]+').tokenize
示例10: create_bag_of_words
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def create_bag_of_words(document_list):
"""
Creates a bag of words representation of the document list given. It removes
the punctuation and the stop words.
:type document_list: list[str]
:param document_list:
:rtype: list[list[str]]
:return:
"""
tokenizer = RegexpTokenizer(r'\w+')
tagger = nltk.PerceptronTagger()
cached_stop_words = set(stopwords.words("english"))
cached_stop_words |= {
't', 'didn', 'doesn', 'haven', 'don', 'aren', 'isn', 've', 'll',
'couldn', 'm', 'hasn', 'hadn', 'won', 'shouldn', 's', 'wasn',
'wouldn'}
body = []
processed = []
for i in range(0, len(document_list)):
body.append(document_list[i].lower())
for entry in body:
row = tokenizer.tokenize(entry)
tagged_words = tagger.tag(row)
nouns = []
for tagged_word in tagged_words:
if tagged_word[1].startswith('NN'):
nouns.append(tagged_word[0])
nouns = [word for word in nouns if word not in cached_stop_words]
processed.append(nouns)
return processed
示例11: test
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def test():
global N, words, network
print 'In testing.'
gettysburg = """Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure. We are met on a great battle-field of that war. We have come to dedicate a portion of that field, as a final resting place for those who here gave their lives that that nation might live. It is altogether fitting and proper that we should do this. But, in a larger sense, we can not dedicate -- we can not consecrate -- we can not hallow -- this ground. The brave men, living and dead, who struggled here, have consecrated it, far above our poor power to add or detract. The world will little note, nor long remember what we say here, but it can never forget what they did here. It is for us the living, rather, to be dedicated here to the unfinished work which they who fought here have thus far so nobly advanced. It is rather for us to be here dedicated to the great task remaining before us -- that from these honored dead we take increased devotion to that cause for which they gave the last full measure of devotion -- that we here highly resolve that these dead shall not have died in vain -- that this nation, under God, shall have a new birth of freedom -- and that government of the people, by the people, for the people, shall not perish from the earth."""
tokenizer = RegexpTokenizer('\w+')
gettysburg_tokens = tokenizer.tokenize(gettysburg)
samples = []
for token in gettysburg_tokens:
word = token.lower()
if word not in ENGLISH_STOP_WORDS and word not in punctuation:
samples.append(word)
dist = FreqDist(samples)
V = Vol(1, 1, N, 0.0)
for i, word in enumerate(words):
V.w[i] = dist.freq(word)
pred = network.forward(V).w
topics = []
while len(topics) != 5:
max_act = max(pred)
topic_idx = pred.index(max_act)
topic = words[topic_idx]
if topic in gettysburg_tokens:
topics.append(topic)
del pred[topic_idx]
print 'Topics of the Gettysburg Address:'
print topics
示例12: get_chat_tokenize
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def get_chat_tokenize():
return nltk.RegexpTokenizer(r'\w+|<sil>|[^\w\s]+').tokenize
示例13: update_terms_stats
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import RegexpTokenizer [as 别名]
def update_terms_stats(terms_fd, json_tweet, lex):
tweet = utils.extract_tweet_from_json(json_tweet)
tweet_terms = []
if tweet is None:
return False
tokenizer = nltk.RegexpTokenizer('\#?[\w\d]+')
doc = tokenizer.tokenize(tweet)
for w_raw in doc:
w = w_raw.strip('\"\'.,;?!:)(@/*&')
if not (w.strip('#')).isalpha():
w_aux = ''
#ignore non-ascii characters
for s in w:
if ord(s) < 128:
w_aux += s
else:
break
w = w_aux
w = w.lower()
if (w not in stopwords.words('english') and w not in set(['rt','http','amp'])) and len(w) in range(3, 16):
if w in lex:
continue
tweet_terms.append(w)
terms_fd.inc(w)
bigrams = nltk.bigrams(tweet_terms)
for b in bigrams:
if b[1]+" "+b[0] in lex or b[0]+" "+b[1] in lex:
continue
if b[1]+" "+b[0] in terms_fd:
terms_fd.inc(b[1]+" "+b[0])
else:
terms_fd.inc(b[0]+" "+b[1])
return True