| # Natural Language Toolkit: RSLP Stemmer | |
| # | |
| # Copyright (C) 2001-2023 NLTK Project | |
| # Author: Tiago Tresoldi <tresoldi@gmail.com> | |
| # URL: <https://www.nltk.org/> | |
| # For license information, see LICENSE.TXT | |
| # This code is based on the algorithm presented in the paper "A Stemming | |
| # Algorithm for the Portuguese Language" by Viviane Moreira Orengo and | |
| # Christian Huyck, which unfortunately I had no access to. The code is a | |
| # Python version, with some minor modifications of mine, to the description | |
| # presented at https://www.webcitation.org/5NnvdIzOb and to the C source code | |
| # available at http://www.inf.ufrgs.br/~arcoelho/rslp/integrando_rslp.html. | |
| # Please note that this stemmer is intended for demonstration and educational | |
| # purposes only. Feel free to write me for any comments, including the | |
| # development of a different and/or better stemmer for Portuguese. I also | |
| # suggest using NLTK's mailing list for Portuguese for any discussion. | |
| # Este código é baseado no algoritmo apresentado no artigo "A Stemming | |
| # Algorithm for the Portuguese Language" de Viviane Moreira Orengo e | |
| # Christian Huyck, o qual infelizmente não tive a oportunidade de ler. O | |
| # código é uma conversão para Python, com algumas pequenas modificações | |
| # minhas, daquele apresentado em https://www.webcitation.org/5NnvdIzOb e do | |
| # código para linguagem C disponível em | |
| # http://www.inf.ufrgs.br/~arcoelho/rslp/integrando_rslp.html. Por favor, | |
| # lembre-se de que este stemmer foi desenvolvido com finalidades unicamente | |
| # de demonstração e didáticas. Sinta-se livre para me escrever para qualquer | |
| # comentário, inclusive sobre o desenvolvimento de um stemmer diferente | |
| # e/ou melhor para o português. Também sugiro utilizar-se a lista de discussão | |
| # do NLTK para o português para qualquer debate. | |
| from nltk.data import load | |
| from nltk.stem.api import StemmerI | |
| class RSLPStemmer(StemmerI): | |
| """ | |
| A stemmer for Portuguese. | |
| >>> from nltk.stem import RSLPStemmer | |
| >>> st = RSLPStemmer() | |
| >>> # opening lines of Erico Verissimo's "Música ao Longe" | |
| >>> text = ''' | |
| ... Clarissa risca com giz no quadro-negro a paisagem que os alunos | |
| ... devem copiar . Uma casinha de porta e janela , em cima duma | |
| ... coxilha .''' | |
| >>> for token in text.split(): # doctest: +NORMALIZE_WHITESPACE | |
| ... print(st.stem(token)) | |
| clariss risc com giz no quadro-negr a pais que os alun dev copi . | |
| uma cas de port e janel , em cim dum coxilh . | |
| """ | |
| def __init__(self): | |
| self._model = [] | |
| self._model.append(self.read_rule("step0.pt")) | |
| self._model.append(self.read_rule("step1.pt")) | |
| self._model.append(self.read_rule("step2.pt")) | |
| self._model.append(self.read_rule("step3.pt")) | |
| self._model.append(self.read_rule("step4.pt")) | |
| self._model.append(self.read_rule("step5.pt")) | |
| self._model.append(self.read_rule("step6.pt")) | |
| def read_rule(self, filename): | |
| rules = load("nltk:stemmers/rslp/" + filename, format="raw").decode("utf8") | |
| lines = rules.split("\n") | |
| lines = [line for line in lines if line != ""] # remove blank lines | |
| lines = [line for line in lines if line[0] != "#"] # remove comments | |
| # NOTE: a simple but ugly hack to make this parser happy with double '\t's | |
| lines = [line.replace("\t\t", "\t") for line in lines] | |
| # parse rules | |
| rules = [] | |
| for line in lines: | |
| rule = [] | |
| tokens = line.split("\t") | |
| # text to be searched for at the end of the string | |
| rule.append(tokens[0][1:-1]) # remove quotes | |
| # minimum stem size to perform the replacement | |
| rule.append(int(tokens[1])) | |
| # text to be replaced into | |
| rule.append(tokens[2][1:-1]) # remove quotes | |
| # exceptions to this rule | |
| rule.append([token[1:-1] for token in tokens[3].split(",")]) | |
| # append to the results | |
| rules.append(rule) | |
| return rules | |
| def stem(self, word): | |
| word = word.lower() | |
| # the word ends in 's'? apply rule for plural reduction | |
| if word[-1] == "s": | |
| word = self.apply_rule(word, 0) | |
| # the word ends in 'a'? apply rule for feminine reduction | |
| if word[-1] == "a": | |
| word = self.apply_rule(word, 1) | |
| # augmentative reduction | |
| word = self.apply_rule(word, 3) | |
| # adverb reduction | |
| word = self.apply_rule(word, 2) | |
| # noun reduction | |
| prev_word = word | |
| word = self.apply_rule(word, 4) | |
| if word == prev_word: | |
| # verb reduction | |
| prev_word = word | |
| word = self.apply_rule(word, 5) | |
| if word == prev_word: | |
| # vowel removal | |
| word = self.apply_rule(word, 6) | |
| return word | |
| def apply_rule(self, word, rule_index): | |
| rules = self._model[rule_index] | |
| for rule in rules: | |
| suffix_length = len(rule[0]) | |
| if word[-suffix_length:] == rule[0]: # if suffix matches | |
| if len(word) >= suffix_length + rule[1]: # if we have minimum size | |
| if word not in rule[3]: # if not an exception | |
| word = word[:-suffix_length] + rule[2] | |
| break | |
| return word | |