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| # Natural Language Toolkit: Stemmers | |
| # | |
| # Copyright (C) 2001-2023 NLTK Project | |
| # Author: Trevor Cohn <[email protected]> | |
| # Edward Loper <[email protected]> | |
| # Steven Bird <[email protected]> | |
| # URL: <https://www.nltk.org/> | |
| # For license information, see LICENSE.TXT | |
| import re | |
| from nltk.stem.api import StemmerI | |
| class RegexpStemmer(StemmerI): | |
| """ | |
| A stemmer that uses regular expressions to identify morphological | |
| affixes. Any substrings that match the regular expressions will | |
| be removed. | |
| >>> from nltk.stem import RegexpStemmer | |
| >>> st = RegexpStemmer('ing$|s$|e$|able$', min=4) | |
| >>> st.stem('cars') | |
| 'car' | |
| >>> st.stem('mass') | |
| 'mas' | |
| >>> st.stem('was') | |
| 'was' | |
| >>> st.stem('bee') | |
| 'bee' | |
| >>> st.stem('compute') | |
| 'comput' | |
| >>> st.stem('advisable') | |
| 'advis' | |
| :type regexp: str or regexp | |
| :param regexp: The regular expression that should be used to | |
| identify morphological affixes. | |
| :type min: int | |
| :param min: The minimum length of string to stem | |
| """ | |
| def __init__(self, regexp, min=0): | |
| if not hasattr(regexp, "pattern"): | |
| regexp = re.compile(regexp) | |
| self._regexp = regexp | |
| self._min = min | |
| def stem(self, word): | |
| if len(word) < self._min: | |
| return word | |
| else: | |
| return self._regexp.sub("", word) | |
| def __repr__(self): | |
| return f"<RegexpStemmer: {self._regexp.pattern!r}>" | |