Named Entity Recognition
Named Entity Recognition (NER) is a subtask of natural language processing that identifies and classifies named entities in text into predefined categories such as person names, organizations, locations, dates, and monetary values. It involves extracting structured information from unstructured text by detecting entity boundaries and assigning them to specific types. NER is a fundamental component in information extraction systems and is widely used in applications like search engines, chatbots, and data mining.
Developers should learn NER when building applications that require extracting structured data from text, such as in document analysis, customer support automation, or social media monitoring. It is essential for tasks like entity linking, knowledge graph construction, and improving search relevance by identifying key terms. NER is particularly valuable in domains like healthcare for extracting medical terms, finance for detecting company names, and legal for identifying case references.