updates

Backend/venv/lib/python3.12/site-packages/nltk/stem/rslp.py

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+# Natural Language Toolkit: RSLP Stemmer
+#
+# Copyright (C) 2001-2025 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