Stop words are commonly used words in a language that are not very important and don’t contribute to understanding the NLP tasks. These words occur frequently in text and removing these words results in efficient processing and analysis. After tokenization, it is good to remove the stop words to increase efficiency and performance. This article […]
In Natural Language Processing (NLP) stemming and lemmatization are text normalization techniques that reduce the words into their root form. This helps improve text analysis and performance in various NLP tasks such as information retrieval and text mining. This article explains both techniques in detail, their application and limitations, and their implementation using Python. Stemming […]
Tokenization is an important step in NLP data preprocessing that involves breaking the text into smaller units called tokens. These tokens can be individual words, characters, or subwords depending on the tokenization strategy. This tutorial provides an in-depth exploration of tokenization techniques, their importance in NLP tasks, and practical examples using Python and popular NLP libraries. […]