Named Entity Recognition (NER) is a natural language processing technique that identifies and classifies named entities in text into predefined categories such as person names, organizations, locations, dates, quantities, and other proper nouns. NER is a fundamental component of information extraction systems and helps structure unstructured text data.

Common entity types include:

• PERSON: Individual names (e.g., "John Smith")
• ORGANIZATION: Company, agency, or institution names (e.g., "Apple Inc.")
• LOCATION: Geographic locations (e.g., "New York City")
• DATE/TIME: Temporal expressions (e.g., "January 2024")
• MONEY: Monetary values (e.g., "$100")
• PERCENT: Percentage values (e.g., "15%")

NER is widely used in information retrieval, content analysis, chatbots, and automated document processing systems.