会出现这样的问题是由于nltk.internals.compile_regexp_to_noncapturing()在V3.1版本的NLTK中已经被抛弃(尽管在更早的版本中它仍然可以运行),为此我们把之前定义的pattern稍作修改(参考:https://blog.csdn.net/baimafujinji/article/details/51051505) 1pattern = r'''(?x) # set flag to allow verbose regex...
1.RegexpTokenizer类 from nltk.tokenize import RegexpTokenizer text = " I won't just survive, Oh, you will see me thrive. Can't write my story,I'm beyond the archetype." # 实例化RegexpTokenizer 会按照正则表达式进行re.findall() regexp_tokenizer = RegexpTokenizer(pattern="\w+") # 实...
text_no_whitespace, '\n') # 分词 tokens = word_tokenize(text_no_whitespace) prin...
from nltk.tokenize import sent_tokenizefrom nltk.tokenize import word_tokenizefrom nltk.corpus import reutersfrom nltk.probability import FreqDistfrom nltk.tokenize import RegexpTokenizerfrom nltk.cluster.util import cosine_distanceimport numpy as npimport networkx as nx # 定义文本摘要函数def summarize(te...
>>> WSTokenizer().tokenize(t, addlocs=True) # break on whitespace >>> print t['TEXT'] This is my first test sentence >>> print t['SUBTOKENS'] [<This>@[0:4c], <is>@[5:7c], <my>@[8:10c], <first>@[11:16c],
word1 = regexp_tokenize(s, pattern="\\w+") print(word1) word2 = regexp_tokenize(s, pattern='\\d+') print(word2) # from nltk.tokenize import blankline_tokenize word3 = blankline_tokenize(s) print(word3) # from nltk.tokenize import wordpunct_tokenize ...
(train_text) tokenized = custom_sent_tokenizer.tokenize(sample_text) def process_content(): try: for i in tokenized[5:]: words = nltk.word_tokenize(i) tagged = nltk.pos_tag(words) chunkGram = r"""Chunk: {<.*>+} }<VB.?|IN|DT|TO>+{""" chunkParser = nltk.RegexpParser(chunk...
(2) NLTK’s Regexp Tokenizer >>> nltk.regexp_tokenize(text, pattern) 7. Segmentation分词,tokenization可以看作segmentation的一个特例 (1) sentence segmentation — sentence level 对于一些成熟语料库,已经按句子(和按词)切分好了,比如: >>> nltk.corpus.brown.sents()>>> nltk.corpus.brown.words() ...
TreebankWordTokenizer,PunktWordTokenizer和WhitespaceTokenizer,并且他们的使用方法与WordPunct tokenizer也相似。然而,显然我们并不满足于此。对于比較复杂的词型,WordPunct tokenizer往往并不胜任。此时我们须要借助正則表達式的强大能力来完毕分词任务。此时我所使用的函数是regexp_tokenize()。
word tokenize 1. NLTK 1. nltk.word_tokenize substring，按标点字符和空格划分，但保留⼩数、分数⼀类 2. nltk.tokenize.RegexpTokenizer 正则可保留固定的⼀部分，如对⼀些⾦钱'$10'表⽰或者其他⾮空⽩序列 3. nltk.tokenize.stanford.StanfordTokenizer 会将单位切分更细，例如：kg/m² -> '...