Source code for nltk.tokenize.util

# Natural Language Toolkit: Tokenizer Utilities
#
# Copyright (C) 2001-2013 NLTK Project
# Author: Steven Bird <stevenbird1@gmail.com>
# URL: <http://nltk.sourceforge.net>
# For license information, see LICENSE.TXT

from re import finditer

[docs]def string_span_tokenize(s, sep): r""" Return the offsets of the tokens in *s*, as a sequence of ``(start, end)`` tuples, by splitting the string at each occurrence of *sep*. >>> from nltk.tokenize.util import string_span_tokenize >>> s = '''Good muffins cost $3.88\nin New York. Please buy me ... two of them.\n\nThanks.''' >>> list(string_span_tokenize(s, " ")) [(0, 4), (5, 12), (13, 17), (18, 26), (27, 30), (31, 36), (37, 37), (38, 44), (45, 48), (49, 55), (56, 58), (59, 73)] :param s: the string to be tokenized :type s: str :param sep: the token separator :type sep: str :rtype: iter(tuple(int, int)) """ if len(sep) == 0: raise ValueError("Token delimiter must not be empty") left = 0 while True: try: right = s.index(sep, left) if right != 0: yield left, right except ValueError: if left != len(s): yield left, len(s) break left = right + len(sep)
[docs]def regexp_span_tokenize(s, regexp): r""" Return the offsets of the tokens in *s*, as a sequence of ``(start, end)`` tuples, by splitting the string at each successive match of *regexp*. >>> from nltk.tokenize import WhitespaceTokenizer >>> s = '''Good muffins cost $3.88\nin New York. Please buy me ... two of them.\n\nThanks.''' >>> list(WhitespaceTokenizer().span_tokenize(s)) [(0, 4), (5, 12), (13, 17), (18, 23), (24, 26), (27, 30), (31, 36), (38, 44), (45, 48), (49, 51), (52, 55), (56, 58), (59, 64), (66, 73)] :param s: the string to be tokenized :type s: str :param regexp: regular expression that matches token separators :type regexp: str :rtype: iter(tuple(int, int)) """ left = 0 for m in finditer(regexp, s): right, next = m.span() if right != 0: yield left, right left = next yield left, len(s)
[docs]def spans_to_relative(spans): r""" Return a sequence of relative spans, given a sequence of spans. >>> from nltk.tokenize import WhitespaceTokenizer >>> from nltk.tokenize.util import spans_to_relative >>> s = '''Good muffins cost $3.88\nin New York. Please buy me ... two of them.\n\nThanks.''' >>> list(spans_to_relative(WhitespaceTokenizer().span_tokenize(s))) [(0, 4), (1, 7), (1, 4), (1, 5), (1, 2), (1, 3), (1, 5), (2, 6), (1, 3), (1, 2), (1, 3), (1, 2), (1, 5), (2, 7)] :param spans: a sequence of (start, end) offsets of the tokens :type spans: iter(tuple(int, int)) :rtype: iter(tuple(int, int)) """ prev = 0 for left, right in spans: yield left - prev, right - left prev = right
if __name__ == "__main__": import doctest doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)