Lemmatization with NLTK:

The process of grouping different inflected forms in a word for analyzing them as a single term is known as lemmatization. It is similar to stemming, but it also brings context to the words. It will link words with similar meaning to one word in python for data science.

We prefer lemmatization over stemming as it morphologically analyzes the words. Sometimes we confuse lemmatization and stemming, but they are different.

Applications of lemmatization:
  • It is there in comprehensive retrieval systems such as search engines.
  • It is also there in compact indexing.

Examples showing lemmatization:

Lemmatization with NLTK in Python for Data Science- PST Analytics

A major difference between lemmatization and stemming is that lemmatization will take part of speech as a parameter, “pos”, in case it is not supply the default will be “noun”.

In the example below we show the implementation of lemmatization using NLTK.

Lemmatization with NLTK in Python for Data Science- PST Analytics

So, to learn more about it in python for data science, you can check this and this as well.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.