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| | from nltk.corpus import wordnet as wn
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| | class WordNetLemmatizer:
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| | """
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| | WordNet Lemmatizer
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| | Lemmatize using WordNet'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
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| | >>> wnl = WordNetLemmatizer()
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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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| | """
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| | def lemmatize(self, word: str, pos: str = "n") -> str:
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| | """Lemmatize `word` using WordNet'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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| | :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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| | :param pos: str
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| | :return: The lemma of `word`, for the given `pos`.
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| | """
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| | lemmas = wn._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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