<ul><li>One-hot encoding is a simple yet foundational method in NLP that converts categorical data, like words, into numerical format.</li><li>It represents each word with a binary vector, where only one position is 'hot' (1) and the rest remain 'cold' (0).</li><li>One-hot encoding is used to transform text data for NLP tasks such as sentiment analysis before feeding it into machine learning algorithms or deep neural networks.</li><li>While one-hot encoding has limitations, it serves as an entry point to more advanced NLP techniques and provides valuable insight into language interpretation by machines.</li></ul>

One-Hot Encoding in NLP: A Gentle Introduction

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