Lemmatization - Nomidl

Day 4: Stemming and Lemmatization

Stemming and lemmatization are two popular techniques for text pre-processing in natural language processing (NLP) tasks. In this article, we will discuss what stemming and lemmatization are and provide examples to illustrate their application. Stemming is the process of reducing a word to its root or stem form. For example, the stem of the word…

Step-by-Step Process of Implementing Stemming and Lemmatization in Python?

Install the Natural Language Toolkit (NLTK) library. This library provides a range of tools for natural language processing, including stemming and lemmatization algorithms. You can install it using pip install nltk. Import the necessary functions from the NLTK library. For example, to use the Porter stemmer, you would use the following import statement: from nltk.stem.porter…

Stemming vs Lemmatization Difference: Explained in Detail

Introduction When dealing with large amount of text data, it becomes essential to preprocess and analyze the text effectively. Stemming and lemmatization are text processing techniques that help reduce words to their base forms, helps you in better analysis and understanding. Stemming Stemming is a technique that aims to reduce words to their root form,…