Source code for nltk.wsd

# Natural Language Toolkit: Word Sense Disambiguation Algorithms
#
# Authors: Liling Tan <alvations@gmail.com>,
#          Dmitrijs Milajevs <dimazest@gmail.com>
#
# Copyright (C) 2001-2018 NLTK Project
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

from nltk.corpus import wordnet


[docs]def lesk(context_sentence, ambiguous_word, pos=None, synsets=None): """Return a synset for an ambiguous word in a context. :param iter context_sentence: The context sentence where the ambiguous word occurs, passed as an iterable of words. :param str ambiguous_word: The ambiguous word that requires WSD. :param str pos: A specified Part-of-Speech (POS). :param iter synsets: Possible synsets of the ambiguous word. :return: ``lesk_sense`` The Synset() object with the highest signature overlaps. This function is an implementation of the original Lesk algorithm (1986) [1]. Usage example:: >>> lesk(['I', 'went', 'to', 'the', 'bank', 'to', 'deposit', 'money', '.'], 'bank', 'n') Synset('savings_bank.n.02') [1] Lesk, Michael. "Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone." Proceedings of the 5th Annual International Conference on Systems Documentation. ACM, 1986. http://dl.acm.org/citation.cfm?id=318728 """ context = set(context_sentence) if synsets is None: synsets = wordnet.synsets(ambiguous_word) if pos: synsets = [ss for ss in synsets if str(ss.pos()) == pos] if not synsets: return None _, sense = max( (len(context.intersection(ss.definition().split())), ss) for ss in synsets ) return sense