79 lines
2.0 KiB
Plaintext
79 lines
2.0 KiB
Plaintext
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.. Copyright (C) 2001-2019 NLTK Project
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.. For license information, see LICENSE.TXT
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==========
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Stemmers
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==========
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Overview
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~~~~~~~~
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Stemmers remove morphological affixes from words, leaving only the
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word stem.
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>>> from __future__ import print_function
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>>> from nltk.stem import *
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Unit tests for the Porter stemmer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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>>> from nltk.stem.porter import *
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Create a new Porter stemmer.
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>>> stemmer = PorterStemmer()
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Test the stemmer on various pluralised words.
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>>> plurals = ['caresses', 'flies', 'dies', 'mules', 'denied',
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... 'died', 'agreed', 'owned', 'humbled', 'sized',
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... 'meeting', 'stating', 'siezing', 'itemization',
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... 'sensational', 'traditional', 'reference', 'colonizer',
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... 'plotted']
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>>> singles = [stemmer.stem(plural) for plural in plurals]
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>>> print(' '.join(singles)) # doctest: +NORMALIZE_WHITESPACE
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caress fli die mule deni die agre own humbl size meet
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state siez item sensat tradit refer colon plot
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Unit tests for Snowball stemmer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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>>> from nltk.stem.snowball import SnowballStemmer
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See which languages are supported.
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>>> print(" ".join(SnowballStemmer.languages))
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arabic danish dutch english finnish french german hungarian italian
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norwegian porter portuguese romanian russian spanish swedish
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Create a new instance of a language specific subclass.
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>>> stemmer = SnowballStemmer("english")
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Stem a word.
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>>> print(stemmer.stem("running"))
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run
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Decide not to stem stopwords.
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>>> stemmer2 = SnowballStemmer("english", ignore_stopwords=True)
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>>> print(stemmer.stem("having"))
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have
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>>> print(stemmer2.stem("having"))
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having
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The 'english' stemmer is better than the original 'porter' stemmer.
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>>> print(SnowballStemmer("english").stem("generously"))
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generous
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>>> print(SnowballStemmer("porter").stem("generously"))
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gener
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.. note::
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Extra stemmer tests can be found in `nltk.test.unit.test_stem`.
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