Named Entity Recognition (NER) is the process of identifying and classifying named entities in text into pre-defined categories such as person, organization, location, date, time, etc. NER is an important task in natural language processing (NLP) because it enables a computer to understand the meaning of text by identifying entities and their relationships.

NER is typically performed using machine learning algorithms that learn from annotated data, which is a corpus of text that has been manually labeled with the correct named entity tags. These algorithms use various features, such as the context in which a named entity appears, its capitalization, and its relationship to other words in the sentence, to predict the correct category for each named entity.

NER can be a challenging task because named entities can appear in various forms, such as full names, abbreviations, acronyms, and nicknames. Moreover, the same named entity can have different meanings depending on the context in which it appears. For example, “Apple” can refer to the fruit or to the technology company.

NER is used in many NLP applications, such as information extraction, question answering, text summarization, and sentiment analysis, among others. By identifying named entities and their categories, NER can help computers to understand the meaning of text and make more accurate predictions and decisions based on that understanding.