Named Entity Recognition (NER) is a natural language processing (NLP) task that involves identifying and categorizing named entities in textual data into predefined categories such as names of people, organizations, locations, dates, and other types of entities. NER is an important component in information extraction, entity linking, and other NLP tasks. It involves processing large amounts of text data to automatically extract and classify named entities, which can be used for various applications such as information retrieval, question answering, and text summarization. NER systems typically use machine learning models such as Conditional Random Fields (CRF) or deep learning models like Bidirectional LSTM-CRF to recognize and classify named entities in text.