Source code for nltk.stem.rslp

# -*- coding: utf-8 -*-

# Natural Language Toolkit: RSLP Stemmer
#
# Copyright (C) 2001-2017 NLTK Project
# Author: Tiago Tresoldi <tresoldi@gmail.com>
# URL: <http://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 http://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 http://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 __future__ import print_function, unicode_literals
from nltk.data import load

from nltk.stem.api import StemmerI

[docs]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(): ... 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") )
[docs] 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
[docs] 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
[docs] 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