NER

Named Entity Recognition

A natural language processing (NLP) technique that involves identifying and extracting named entities from text data. Named entities are words or phrases that refer to specific entities such as people, organizations, locations, dates, and more.

NER is used to automatically identify and extract named entities from text data, and to classify them into predefined categories such as person, organization, and location. This can be useful for a range of applications such as information retrieval, text mining, and sentiment analysis.

NER typically involves training a machine learning model on a large corpus of annotated text data, in order to teach the model how to recognize and classify different types of named entities. The model can then be applied to new text data to automatically extract and classify named entities.

NER is an important technique for a wide range of industries, including finance, healthcare, and legal, where large amounts of unstructured text data need to be analyzed and processed. By using NER to automatically extract named entities from text data, businesses can improve their information retrieval and analysis capabilities, and gain valuable insights into their data.

• Abbreviation: NER