# Natural Language Toolkit: Simple Tokenizers
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
# Steven Bird <stevenbird1@gmail.com>
# URL: <https://www.nltk.org>
# For license information, see LICENSE.TXT
r"""
Simple Tokenizers
These tokenizers divide strings into substrings using the string
``split()`` method.
When tokenizing using a particular delimiter string, use
the string ``split()`` method directly, as this is more efficient.
The simple tokenizers are *not* available as separate functions;
instead, you should just use the string ``split()`` method directly:
>>> s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks."
>>> s.split() # doctest: +NORMALIZE_WHITESPACE
['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York.',
'Please', 'buy', 'me', 'two', 'of', 'them.', 'Thanks.']
>>> s.split(' ') # doctest: +NORMALIZE_WHITESPACE
['Good', 'muffins', 'cost', '$3.88\nin', 'New', 'York.', '',
'Please', 'buy', 'me\ntwo', 'of', 'them.\n\nThanks.']
>>> s.split('\n') # doctest: +NORMALIZE_WHITESPACE
['Good muffins cost $3.88', 'in New York. Please buy me',
'two of them.', '', 'Thanks.']
The simple tokenizers are mainly useful because they follow the
standard ``TokenizerI`` interface, and so can be used with any code
that expects a tokenizer. For example, these tokenizers can be used
to specify the tokenization conventions when building a `CorpusReader`.
"""
from nltk.tokenize.api import StringTokenizer, TokenizerI
from nltk.tokenize.util import regexp_span_tokenize, string_span_tokenize
[docs]class SpaceTokenizer(StringTokenizer):
r"""Tokenize a string using the space character as a delimiter,
which is the same as ``s.split(' ')``.
>>> from nltk.tokenize import SpaceTokenizer
>>> s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks."
>>> SpaceTokenizer().tokenize(s) # doctest: +NORMALIZE_WHITESPACE
['Good', 'muffins', 'cost', '$3.88\nin', 'New', 'York.', '',
'Please', 'buy', 'me\ntwo', 'of', 'them.\n\nThanks.']
"""
_string = " "
[docs]class TabTokenizer(StringTokenizer):
r"""Tokenize a string use the tab character as a delimiter,
the same as ``s.split('\t')``.
>>> from nltk.tokenize import TabTokenizer
>>> TabTokenizer().tokenize('a\tb c\n\t d')
['a', 'b c\n', ' d']
"""
_string = "\t"
[docs]class CharTokenizer(StringTokenizer):
"""Tokenize a string into individual characters. If this functionality
is ever required directly, use ``for char in string``.
"""
[docs] def tokenize(self, s):
return list(s)
[docs] def span_tokenize(self, s):
yield from enumerate(range(1, len(s) + 1))
[docs]class LineTokenizer(TokenizerI):
r"""Tokenize a string into its lines, optionally discarding blank lines.
This is similar to ``s.split('\n')``.
>>> from nltk.tokenize import LineTokenizer
>>> s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks."
>>> LineTokenizer(blanklines='keep').tokenize(s) # doctest: +NORMALIZE_WHITESPACE
['Good muffins cost $3.88', 'in New York. Please buy me',
'two of them.', '', 'Thanks.']
>>> # same as [l for l in s.split('\n') if l.strip()]:
>>> LineTokenizer(blanklines='discard').tokenize(s) # doctest: +NORMALIZE_WHITESPACE
['Good muffins cost $3.88', 'in New York. Please buy me',
'two of them.', 'Thanks.']
:param blanklines: Indicates how blank lines should be handled. Valid values are:
- ``discard``: strip blank lines out of the token list before returning it.
A line is considered blank if it contains only whitespace characters.
- ``keep``: leave all blank lines in the token list.
- ``discard-eof``: if the string ends with a newline, then do not generate
a corresponding token ``''`` after that newline.
"""
[docs] def __init__(self, blanklines="discard"):
valid_blanklines = ("discard", "keep", "discard-eof")
if blanklines not in valid_blanklines:
raise ValueError(
"Blank lines must be one of: %s" % " ".join(valid_blanklines)
)
self._blanklines = blanklines
[docs] def tokenize(self, s):
lines = s.splitlines()
# If requested, strip off blank lines.
if self._blanklines == "discard":
lines = [l for l in lines if l.rstrip()]
elif self._blanklines == "discard-eof":
if lines and not lines[-1].strip():
lines.pop()
return lines
# discard-eof not implemented
[docs] def span_tokenize(self, s):
if self._blanklines == "keep":
yield from string_span_tokenize(s, r"\n")
else:
yield from regexp_span_tokenize(s, r"\n(\s+\n)*")
######################################################################
# { Tokenization Functions
######################################################################
# XXX: it is stated in module docs that there is no function versions
[docs]def line_tokenize(text, blanklines="discard"):
return LineTokenizer(blanklines).tokenize(text)