90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
# Natural Language Toolkit: WordNet stemmer interface
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#
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# Copyright (C) 2001-2024 NLTK Project
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# Author: Steven Bird <stevenbird1@gmail.com>
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# Edward Loper <edloper@gmail.com>
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# Eric Kafe <kafe.eric@gmail.com>
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# URL: <https://www.nltk.org/>
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# For license information, see LICENSE.TXT
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class WordNetLemmatizer:
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"""
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WordNet Lemmatizer
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Provides 3 lemmatizer modes: _morphy(), morphy() and lemmatize().
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lemmatize() is a permissive wrapper around _morphy().
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It returns the shortest lemma found in WordNet,
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or the input string unchanged if nothing is found.
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>>> from nltk.stem import WordNetLemmatizer as wnl
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>>> print(wnl().lemmatize('us', 'n'))
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u
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>>> print(wnl().lemmatize('Anythinggoeszxcv'))
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Anythinggoeszxcv
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"""
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def _morphy(self, form, pos, check_exceptions=True):
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"""
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_morphy() is WordNet's _morphy lemmatizer.
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It returns a list of all lemmas found in WordNet.
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>>> from nltk.stem import WordNetLemmatizer as wnl
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>>> print(wnl()._morphy('us', 'n'))
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['us', 'u']
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"""
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from nltk.corpus import wordnet as wn
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return wn._morphy(form, pos, check_exceptions)
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def morphy(self, form, pos=None, check_exceptions=True):
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"""
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morphy() is a restrictive wrapper around _morphy().
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It returns the first lemma found in WordNet,
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or None if no lemma is found.
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>>> from nltk.stem import WordNetLemmatizer as wnl
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>>> print(wnl().morphy('us', 'n'))
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us
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>>> print(wnl().morphy('catss'))
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None
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"""
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from nltk.corpus import wordnet as wn
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return wn.morphy(form, pos, check_exceptions)
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def lemmatize(self, word: str, pos: str = "n") -> str:
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"""Lemmatize `word` by picking the shortest of the possible lemmas,
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using the wordnet corpus reader's built-in _morphy function.
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Returns the input word unchanged if it cannot be found in WordNet.
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>>> from nltk.stem import WordNetLemmatizer as wnl
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>>> print(wnl().lemmatize('dogs'))
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dog
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>>> print(wnl().lemmatize('churches'))
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church
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>>> print(wnl().lemmatize('aardwolves'))
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aardwolf
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>>> print(wnl().lemmatize('abaci'))
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abacus
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>>> print(wnl().lemmatize('hardrock'))
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hardrock
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:param word: The input word to lemmatize.
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:type word: str
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:param pos: The Part Of Speech tag. Valid options are `"n"` for nouns,
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`"v"` for verbs, `"a"` for adjectives, `"r"` for adverbs and `"s"`
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for satellite adjectives.
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:type pos: str
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:return: The shortest lemma of `word`, for the given `pos`.
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"""
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lemmas = self._morphy(word, pos)
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return min(lemmas, key=len) if lemmas else word
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def __repr__(self):
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return "<WordNetLemmatizer>"
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