Articles → NLP → Stemming In NLP

Stemming In NLP

This article describes stemming in NLP.

Purpose

In NLP, stemming is the process of reducing words to their root form (called the stem) by cutting off prefixes or suffixes. For example:

playing, played, plays → play
connection, connected, connecting → connect

Types Of Stemmers

For this article, we will discuss three types of stemmers.

Porter Stemmer
Snowball Stemmer
Lancaster Stemmer

Porter Stemmer

This is one of the most widely used stemmers. It employs a set of rules to remove common word endings, such as -ing, -ed, and -ly. It produces stems that are not always valid words.

connection, connected, connecting → connect
happiness → happi

Snowball Stemmer

Snowball stemmer is an improved version of Porter Stemmer. It supports multiple languages (English, French, German, etc.).

Lancaster Stemmer

It is a very aggressive stemmer. It may cut too much and distort words.

Example

Consider the following example: -

from nltk.stem import PorterStemmer, LancasterStemmer, SnowballStemmer

porter = PorterStemmer()
lancaster = LancasterStemmer()
snowball = SnowballStemmer("english")

words = ["playing", "played", "happiness", "studies"]

for w in words:
    print(f"{w} -> Porter:{porter.stem(w)}, Lancaster:{lancaster.stem(w)}, Snowball:{snowball.stem(w)}")

Output

Posted By -	Karan Gupta

Posted On -	Monday, August 25, 2025

Query/Feedback

Your Email Id		**

Subject		*

Query/Feedback	Characters remaining 250	**