Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP). It involves identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, dates, etc. NER plays an important role in various NLP applications such as information retrieval, text classification, question answering, sentiment analysis, and many more. In this article, we will discuss the basics of Named Entity Recognition and how it works in NLP. Source: https://www.shaip.com/named-entity-recognition-and-its-types/ How Does NER Work? NER involves analyzing a piece of text to identify and classify named entities into predefined categories. The process of NER can be divided into the following steps: Tokenization : The first step in NER is to break down the text into individual words or tokens. This is known as tokenization. The tokens are then used as the basic units for analysis. Part-of-speech tagging: The next step is to identify the pa