52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
# Natural Language Toolkit: Word Sense Disambiguation Algorithms
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#
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# Authors: Liling Tan <alvations@gmail.com>,
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# Dmitrijs Milajevs <dimazest@gmail.com>
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#
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# Copyright (C) 2001-2019 NLTK Project
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from nltk.corpus import wordnet
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def lesk(context_sentence, ambiguous_word, pos=None, synsets=None):
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"""Return a synset for an ambiguous word in a context.
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:param iter context_sentence: The context sentence where the ambiguous word
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occurs, passed as an iterable of words.
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:param str ambiguous_word: The ambiguous word that requires WSD.
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:param str pos: A specified Part-of-Speech (POS).
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:param iter synsets: Possible synsets of the ambiguous word.
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:return: ``lesk_sense`` The Synset() object with the highest signature overlaps.
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This function is an implementation of the original Lesk algorithm (1986) [1].
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Usage example::
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>>> lesk(['I', 'went', 'to', 'the', 'bank', 'to', 'deposit', 'money', '.'], 'bank', 'n')
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Synset('savings_bank.n.02')
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[1] Lesk, Michael. "Automatic sense disambiguation using machine
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readable dictionaries: how to tell a pine cone from an ice cream
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cone." Proceedings of the 5th Annual International Conference on
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Systems Documentation. ACM, 1986.
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http://dl.acm.org/citation.cfm?id=318728
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"""
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context = set(context_sentence)
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if synsets is None:
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synsets = wordnet.synsets(ambiguous_word)
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if pos:
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synsets = [ss for ss in synsets if str(ss.pos()) == pos]
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if not synsets:
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return None
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_, sense = max(
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(len(context.intersection(ss.definition().split())), ss) for ss in synsets
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)
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return sense
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