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Backend/venv/lib/python3.12/site-packages/nltk/wsd.py

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+# Natural Language Toolkit: Word Sense Disambiguation Algorithms
+#
+# Authors: Liling Tan <alvations@gmail.com>,
+#          Dmitrijs Milajevs <dimazest@gmail.com>
+#
+# Copyright (C) 2001-2025 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, lang="eng"):
+    """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.
+    :param str lang: WordNet language.
+    :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('depository_financial_institution.n.01')
+
+    [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, lang=lang)
+
+    if pos:
+        synsets = [ss for ss in synsets if str(ss.pos()) == pos]
+
+    if not synsets:
+        return None
+
+    sense = max(
+        synsets, key=lambda ss: len(context.intersection(ss.definition().split()))
+    )
+
+    return sense