NER
Short for named-entity recognition, NER is a process in natural language processing that involves identifying and classifying named entities in text into predefined categories. These categories may include people's names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. The goal of NER is to pull structured information from unstructured text. For example, in the statement "Computer Hope is located in Salt Lake City, Utah," NER would identify "Computer Hope" as an organization and "Salt Lake City, Utah" as a location.
NER is an essential step in many of the tasks carried out by natural language processors, such as information retrieval, question answering, and text summarization. It helps AI (artificial intelligence) "understand" the context and relationships between entities in a section of text.