221 lines
7.9 KiB
Python
221 lines
7.9 KiB
Python
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# Natural Language Toolkit: Tokenizers
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#
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# Copyright (C) 2001-2023 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# Steven Bird <stevenbird1@gmail.com>
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# Trevor Cohn <tacohn@csse.unimelb.edu.au>
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# URL: <https://www.nltk.org>
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# For license information, see LICENSE.TXT
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r"""
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Regular-Expression Tokenizers
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A ``RegexpTokenizer`` splits a string into substrings using a regular expression.
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For example, the following tokenizer forms tokens out of alphabetic sequences,
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money expressions, and any other non-whitespace sequences:
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>>> from nltk.tokenize import RegexpTokenizer
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>>> s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks."
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>>> tokenizer = RegexpTokenizer(r'\w+|\$[\d\.]+|\S+')
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>>> tokenizer.tokenize(s) # doctest: +NORMALIZE_WHITESPACE
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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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A ``RegexpTokenizer`` can use its regexp to match delimiters instead:
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>>> tokenizer = RegexpTokenizer(r'\s+', gaps=True)
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>>> tokenizer.tokenize(s) # doctest: +NORMALIZE_WHITESPACE
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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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Note that empty tokens are not returned when the delimiter appears at
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the start or end of the string.
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The material between the tokens is discarded. For example,
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the following tokenizer selects just the capitalized words:
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>>> capword_tokenizer = RegexpTokenizer(r'[A-Z]\w+')
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>>> capword_tokenizer.tokenize(s)
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['Good', 'New', 'York', 'Please', 'Thanks']
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This module contains several subclasses of ``RegexpTokenizer``
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that use pre-defined regular expressions.
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>>> from nltk.tokenize import BlanklineTokenizer
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>>> # Uses '\s*\n\s*\n\s*':
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>>> BlanklineTokenizer().tokenize(s) # doctest: +NORMALIZE_WHITESPACE
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['Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.',
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'Thanks.']
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All of the regular expression tokenizers are also available as functions:
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>>> from nltk.tokenize import regexp_tokenize, wordpunct_tokenize, blankline_tokenize
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>>> regexp_tokenize(s, pattern=r'\w+|\$[\d\.]+|\S+') # doctest: +NORMALIZE_WHITESPACE
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['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York', '.',
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'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
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>>> wordpunct_tokenize(s) # doctest: +NORMALIZE_WHITESPACE
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['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York',
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'.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
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>>> blankline_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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Caution: The function ``regexp_tokenize()`` takes the text as its
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first argument, and the regular expression pattern as its second
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argument. This differs from the conventions used by Python's
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``re`` functions, where the pattern is always the first argument.
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(This is for consistency with the other NLTK tokenizers.)
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"""
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import re
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from nltk.tokenize.api import TokenizerI
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from nltk.tokenize.util import regexp_span_tokenize
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class RegexpTokenizer(TokenizerI):
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r"""
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A tokenizer that splits a string using a regular expression, which
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matches either the tokens or the separators between tokens.
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>>> tokenizer = RegexpTokenizer(r'\w+|\$[\d\.]+|\S+')
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:type pattern: str
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:param pattern: The pattern used to build this tokenizer.
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(This pattern must not contain capturing parentheses;
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Use non-capturing parentheses, e.g. (?:...), instead)
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:type gaps: bool
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:param gaps: True if this tokenizer's pattern should be used
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to find separators between tokens; False if this
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tokenizer's pattern should be used to find the tokens
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themselves.
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:type discard_empty: bool
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:param discard_empty: True if any empty tokens `''`
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generated by the tokenizer should be discarded. Empty
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tokens can only be generated if `_gaps == True`.
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:type flags: int
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:param flags: The regexp flags used to compile this
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tokenizer's pattern. By default, the following flags are
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used: `re.UNICODE | re.MULTILINE | re.DOTALL`.
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"""
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def __init__(
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self,
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pattern,
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gaps=False,
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discard_empty=True,
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flags=re.UNICODE | re.MULTILINE | re.DOTALL,
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):
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# If they gave us a regexp object, extract the pattern.
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pattern = getattr(pattern, "pattern", pattern)
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self._pattern = pattern
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self._gaps = gaps
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self._discard_empty = discard_empty
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self._flags = flags
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self._regexp = None
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def _check_regexp(self):
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if self._regexp is None:
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self._regexp = re.compile(self._pattern, self._flags)
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def tokenize(self, text):
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self._check_regexp()
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# If our regexp matches gaps, use re.split:
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if self._gaps:
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if self._discard_empty:
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return [tok for tok in self._regexp.split(text) if tok]
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else:
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return self._regexp.split(text)
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# If our regexp matches tokens, use re.findall:
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else:
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return self._regexp.findall(text)
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def span_tokenize(self, text):
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self._check_regexp()
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if self._gaps:
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for left, right in regexp_span_tokenize(text, self._regexp):
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if not (self._discard_empty and left == right):
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yield left, right
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else:
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for m in re.finditer(self._regexp, text):
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yield m.span()
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def __repr__(self):
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return "{}(pattern={!r}, gaps={!r}, discard_empty={!r}, flags={!r})".format(
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self.__class__.__name__,
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self._pattern,
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self._gaps,
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self._discard_empty,
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self._flags,
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)
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class WhitespaceTokenizer(RegexpTokenizer):
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r"""
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Tokenize a string on whitespace (space, tab, newline).
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In general, users should use the string ``split()`` method instead.
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>>> from nltk.tokenize import WhitespaceTokenizer
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>>> s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks."
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>>> WhitespaceTokenizer().tokenize(s) # doctest: +NORMALIZE_WHITESPACE
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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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"""
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def __init__(self):
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RegexpTokenizer.__init__(self, r"\s+", gaps=True)
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class BlanklineTokenizer(RegexpTokenizer):
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"""
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Tokenize a string, treating any sequence of blank lines as a delimiter.
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Blank lines are defined as lines containing no characters, except for
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space or tab characters.
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"""
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def __init__(self):
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RegexpTokenizer.__init__(self, r"\s*\n\s*\n\s*", gaps=True)
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class WordPunctTokenizer(RegexpTokenizer):
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r"""
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Tokenize a text into a sequence of alphabetic and
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non-alphabetic characters, using the regexp ``\w+|[^\w\s]+``.
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>>> from nltk.tokenize import WordPunctTokenizer
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>>> s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks."
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>>> WordPunctTokenizer().tokenize(s) # doctest: +NORMALIZE_WHITESPACE
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['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York',
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'.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
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"""
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def __init__(self):
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RegexpTokenizer.__init__(self, r"\w+|[^\w\s]+")
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######################################################################
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# { Tokenization Functions
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######################################################################
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def regexp_tokenize(
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text,
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pattern,
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gaps=False,
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discard_empty=True,
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flags=re.UNICODE | re.MULTILINE | re.DOTALL,
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):
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"""
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Return a tokenized copy of *text*. See :class:`.RegexpTokenizer`
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for descriptions of the arguments.
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"""
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tokenizer = RegexpTokenizer(pattern, gaps, discard_empty, flags)
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return tokenizer.tokenize(text)
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blankline_tokenize = BlanklineTokenizer().tokenize
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wordpunct_tokenize = WordPunctTokenizer().tokenize
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