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