What is the nltk.stem.wordnet module in Python?

Text normalization (i.e., preparing text, words and documents) is one of the most fundamental tasks of Natural Language processing field. These text normalization techniques are called Stemming and Lemmatization. nltk.stem is one of the most widely used libraries for python for Stemming and Lemmitization.

Examples of Stemming and Lemmitization:

Now, the words cars, car’s, CAR, Car, and cars’ are all derived from the rootor stem word car. With nltk.stem, all of these words will be mapped to car.

Wordnet module

Stemming and Lemmatization is pretty much the same thing. However, Lemmatization is more efficient as it allows for more context so that all words with similar meanings are grouped as one. For example,

• rocks : rock
• corpora : corpus
• better : good

Lemmatization is widely used technique in search engines and other retrieval systems.

Moreover, lemmatization introduces a pospart of speech parameter.

By default, pos takes the value “noun”.

Code

// import libraries
from nltk.stem import WordNetLemmatizer

// create an instance of the class. 
lemmatizer = WordNetLemmatizer()
  
>> lemmatizer.lemmatize("rocks")
rock
 >> lemmatizer.lemmatize("corpora")
corpus 
// pos parameter is given. "a" refers to as adjective. 
>> lemmatizer.lemmatize("better", pos ="a")
good