130 lines
5.5 KiB
Python
130 lines
5.5 KiB
Python
# -*- coding: utf-8 -*-
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# Natural Language Toolkit: Tokenizers
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# Steven Bird <stevenbird1@gmail.com> (minor additions)
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# Contributors: matthewmc, clouds56
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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r"""
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NLTK Tokenizer Package
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Tokenizers divide strings into lists of substrings. For example,
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tokenizers can be used to find the words and punctuation in a string:
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>>> from nltk.tokenize import word_tokenize
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>>> s = '''Good muffins cost $3.88\nin New York. Please buy me
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... two of them.\n\nThanks.'''
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>>> word_tokenize(s)
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['Good', 'muffins', 'cost', '$', '3.88', 'in', 'New', 'York', '.',
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'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
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This particular tokenizer requires the Punkt sentence tokenization
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models to be installed. NLTK also provides a simpler,
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regular-expression based tokenizer, which splits text on whitespace
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and punctuation:
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>>> from nltk.tokenize import wordpunct_tokenize
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>>> wordpunct_tokenize(s)
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['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York', '.',
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'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
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We can also operate at the level of sentences, using the sentence
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tokenizer directly as follows:
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>>> from nltk.tokenize import sent_tokenize, word_tokenize
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>>> sent_tokenize(s)
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['Good muffins cost $3.88\nin New York.', 'Please buy me\ntwo of them.', 'Thanks.']
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>>> [word_tokenize(t) for t in sent_tokenize(s)]
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[['Good', 'muffins', 'cost', '$', '3.88', 'in', 'New', 'York', '.'],
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['Please', 'buy', 'me', 'two', 'of', 'them', '.'], ['Thanks', '.']]
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Caution: when tokenizing a Unicode string, make sure you are not
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using an encoded version of the string (it may be necessary to
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decode it first, e.g. with ``s.decode("utf8")``.
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NLTK tokenizers can produce token-spans, represented as tuples of integers
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having the same semantics as string slices, to support efficient comparison
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of tokenizers. (These methods are implemented as generators.)
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>>> from nltk.tokenize import WhitespaceTokenizer
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>>> list(WhitespaceTokenizer().span_tokenize(s))
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[(0, 4), (5, 12), (13, 17), (18, 23), (24, 26), (27, 30), (31, 36), (38, 44),
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(45, 48), (49, 51), (52, 55), (56, 58), (59, 64), (66, 73)]
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There are numerous ways to tokenize text. If you need more control over
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tokenization, see the other methods provided in this package.
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For further information, please see Chapter 3 of the NLTK book.
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"""
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import re
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from nltk.data import load
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from nltk.tokenize.casual import (TweetTokenizer, casual_tokenize)
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from nltk.tokenize.mwe import MWETokenizer
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from nltk.tokenize.punkt import PunktSentenceTokenizer
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from nltk.tokenize.regexp import (RegexpTokenizer, WhitespaceTokenizer,
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BlanklineTokenizer, WordPunctTokenizer,
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wordpunct_tokenize, regexp_tokenize,
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blankline_tokenize)
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from nltk.tokenize.repp import ReppTokenizer
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from nltk.tokenize.sexpr import SExprTokenizer, sexpr_tokenize
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from nltk.tokenize.simple import (SpaceTokenizer, TabTokenizer, LineTokenizer,
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line_tokenize)
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from nltk.tokenize.texttiling import TextTilingTokenizer
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from nltk.tokenize.toktok import ToktokTokenizer
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from nltk.tokenize.treebank import TreebankWordTokenizer
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from nltk.tokenize.util import string_span_tokenize, regexp_span_tokenize
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from nltk.tokenize.stanford_segmenter import StanfordSegmenter
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# Standard sentence tokenizer.
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def sent_tokenize(text, language='english'):
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"""
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Return a sentence-tokenized copy of *text*,
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using NLTK's recommended sentence tokenizer
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(currently :class:`.PunktSentenceTokenizer`
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for the specified language).
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:param text: text to split into sentences
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:param language: the model name in the Punkt corpus
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"""
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tokenizer = load('tokenizers/punkt/{0}.pickle'.format(language))
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return tokenizer.tokenize(text)
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# Standard word tokenizer.
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_treebank_word_tokenizer = TreebankWordTokenizer()
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# See discussion on https://github.com/nltk/nltk/pull/1437
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# Adding to TreebankWordTokenizer, the splits on
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# - chervon quotes u'\xab' and u'\xbb' .
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# - unicode quotes u'\u2018', u'\u2019', u'\u201c' and u'\u201d'
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improved_open_quote_regex = re.compile(u'([«“‘„]|[`]+)', re.U)
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improved_close_quote_regex = re.compile(u'([»”’])', re.U)
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improved_punct_regex = re.compile(r'([^\.])(\.)([\]\)}>"\'' u'»”’ ' r']*)\s*$', re.U)
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_treebank_word_tokenizer.STARTING_QUOTES.insert(0, (improved_open_quote_regex, r' \1 '))
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_treebank_word_tokenizer.ENDING_QUOTES.insert(0, (improved_close_quote_regex, r' \1 '))
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_treebank_word_tokenizer.PUNCTUATION.insert(0, (improved_punct_regex, r'\1 \2 \3 '))
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def word_tokenize(text, language='english', preserve_line=False):
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"""
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Return a tokenized copy of *text*,
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using NLTK's recommended word tokenizer
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(currently an improved :class:`.TreebankWordTokenizer`
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along with :class:`.PunktSentenceTokenizer`
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for the specified language).
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:param text: text to split into words
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:type text: str
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:param language: the model name in the Punkt corpus
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:type language: str
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:param preserve_line: An option to keep the preserve the sentence and not sentence tokenize it.
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:type preserver_line: bool
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"""
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sentences = [text] if preserve_line else sent_tokenize(text, language)
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return [token for sent in sentences
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for token in _treebank_word_tokenizer.tokenize(sent)]
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