138 lines
5.2 KiB
Python
138 lines
5.2 KiB
Python
# 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
|