141 lines
5.2 KiB
Python
141 lines
5.2 KiB
Python
# -*- coding: utf-8 -*-
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# Natural Language Toolkit: RSLP Stemmer
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Tiago Tresoldi <tresoldi@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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# This code is based on the algorithm presented in the paper "A Stemming
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# Algorithm for the Portuguese Language" by Viviane Moreira Orengo and
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# Christian Huyck, which unfortunately I had no access to. The code is a
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# Python version, with some minor modifications of mine, to the description
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# presented at http://www.webcitation.org/5NnvdIzOb and to the C source code
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# available at http://www.inf.ufrgs.br/~arcoelho/rslp/integrando_rslp.html.
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# Please note that this stemmer is intended for demonstration and educational
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# purposes only. Feel free to write me for any comments, including the
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# development of a different and/or better stemmer for Portuguese. I also
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# suggest using NLTK's mailing list for Portuguese for any discussion.
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# Este código é baseado no algoritmo apresentado no artigo "A Stemming
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# Algorithm for the Portuguese Language" de Viviane Moreira Orengo e
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# Christian Huyck, o qual infelizmente não tive a oportunidade de ler. O
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# código é uma conversão para Python, com algumas pequenas modificações
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# minhas, daquele apresentado em http://www.webcitation.org/5NnvdIzOb e do
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# código para linguagem C disponível em
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# http://www.inf.ufrgs.br/~arcoelho/rslp/integrando_rslp.html. Por favor,
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# lembre-se de que este stemmer foi desenvolvido com finalidades unicamente
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# de demonstração e didáticas. Sinta-se livre para me escrever para qualquer
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# comentário, inclusive sobre o desenvolvimento de um stemmer diferente
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# e/ou melhor para o português. Também sugiro utilizar-se a lista de discussão
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# do NLTK para o português para qualquer debate.
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from nltk.data import load
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from nltk.stem.api import StemmerI
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class RSLPStemmer(StemmerI):
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"""
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A stemmer for Portuguese.
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>>> from nltk.stem import RSLPStemmer
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>>> st = RSLPStemmer()
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>>> # opening lines of Erico Verissimo's "Música ao Longe"
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>>> text = '''
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... Clarissa risca com giz no quadro-negro a paisagem que os alunos
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... devem copiar . Uma casinha de porta e janela , em cima duma
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... coxilha .'''
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>>> for token in text.split():
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... print(st.stem(token))
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clariss risc com giz no quadro-negr a pais que os alun dev copi .
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uma cas de port e janel , em cim dum coxilh .
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"""
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def __init__(self):
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self._model = []
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self._model.append(self.read_rule("step0.pt"))
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self._model.append(self.read_rule("step1.pt"))
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self._model.append(self.read_rule("step2.pt"))
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self._model.append(self.read_rule("step3.pt"))
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self._model.append(self.read_rule("step4.pt"))
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self._model.append(self.read_rule("step5.pt"))
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self._model.append(self.read_rule("step6.pt"))
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def read_rule(self, filename):
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rules = load("nltk:stemmers/rslp/" + filename, format="raw").decode("utf8")
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lines = rules.split("\n")
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lines = [line for line in lines if line != ""] # remove blank lines
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lines = [line for line in lines if line[0] != "#"] # remove comments
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# NOTE: a simple but ugly hack to make this parser happy with double '\t's
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lines = [line.replace("\t\t", "\t") for line in lines]
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# parse rules
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rules = []
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for line in lines:
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rule = []
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tokens = line.split("\t")
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# text to be searched for at the end of the string
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rule.append(tokens[0][1:-1]) # remove quotes
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# minimum stem size to perform the replacement
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rule.append(int(tokens[1]))
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# text to be replaced into
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rule.append(tokens[2][1:-1]) # remove quotes
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# exceptions to this rule
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rule.append([token[1:-1] for token in tokens[3].split(",")])
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# append to the results
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rules.append(rule)
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return rules
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def stem(self, word):
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word = word.lower()
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# the word ends in 's'? apply rule for plural reduction
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if word[-1] == "s":
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word = self.apply_rule(word, 0)
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# the word ends in 'a'? apply rule for feminine reduction
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if word[-1] == "a":
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word = self.apply_rule(word, 1)
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# augmentative reduction
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word = self.apply_rule(word, 3)
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# adverb reduction
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word = self.apply_rule(word, 2)
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# noun reduction
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prev_word = word
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word = self.apply_rule(word, 4)
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if word == prev_word:
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# verb reduction
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prev_word = word
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word = self.apply_rule(word, 5)
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if word == prev_word:
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# vowel removal
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word = self.apply_rule(word, 6)
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return word
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def apply_rule(self, word, rule_index):
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rules = self._model[rule_index]
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for rule in rules:
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suffix_length = len(rule[0])
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if word[-suffix_length:] == rule[0]: # if suffix matches
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if len(word) >= suffix_length + rule[1]: # if we have minimum size
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if word not in rule[3]: # if not an exception
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word = word[:-suffix_length] + rule[2]
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break
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return word
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