nltk.stem.wordnet module

class nltk.stem.wordnet.WordNetLemmatizer

Bases: object

WordNet Lemmatizer

Provides 3 lemmatizer modes: _morphy(), morphy() and lemmatize().

lemmatize() is a permissive wrapper around _morphy(). It returns the shortest lemma found in WordNet, or the input string unchanged if nothing is found.

>>> from nltk.stem import WordNetLemmatizer as wnl
>>> print(wnl().lemmatize('us', 'n'))
u

>>> print(wnl().lemmatize('Anythinggoeszxcv'))
Anythinggoeszxcv

lemmatize(word: str, pos: str = 'n') → str

Lemmatize word by picking the shortest of the possible lemmas, using the wordnet corpus reader’s built-in _morphy function. Returns the input word unchanged if it cannot be found in WordNet.

>>> from nltk.stem import WordNetLemmatizer as wnl
>>> print(wnl().lemmatize('dogs'))
dog
>>> print(wnl().lemmatize('churches'))
church
>>> print(wnl().lemmatize('aardwolves'))
aardwolf
>>> print(wnl().lemmatize('abaci'))
abacus
>>> print(wnl().lemmatize('hardrock'))
hardrock

Parameters:

• word (str) – The input word to lemmatize.

• pos (str) – The Part Of Speech tag. Valid options are “n” for nouns, “v” for verbs, “a” for adjectives, “r” for adverbs and “s” for satellite adjectives.

Returns:

The shortest lemma of word, for the given pos.

Return type:

str

morphy(form, pos=None, check_exceptions=True)

morphy() is a restrictive wrapper around _morphy(). It returns the first lemma found in WordNet, or None if no lemma is found.

>>> from nltk.stem import WordNetLemmatizer as wnl
>>> print(wnl().morphy('us', 'n'))
us

>>> print(wnl().morphy('catss'))
None