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| # Natural Language Toolkit: Word Sense Disambiguation Algorithms | |
| # | |
| # Authors: Liling Tan <[email protected]>, | |
| # Dmitrijs Milajevs <[email protected]> | |
| # | |
| # Copyright (C) 2001-2023 NLTK Project | |
| # URL: <https://www.nltk.org/> | |
| # For license information, see LICENSE.TXT | |
| from nltk.corpus import wordnet | |
| 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. | |
| https://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 | |