Named Entity Recognition › Named Entity Recognition

Text is full of references to real-world things: people, companies, countries, dates, dollar amounts. Named Entity Recognition (NER) is the task of automatically identifying these mentions in raw text and classifying them into predefined categories such as person, organization, or location.

Consider the sentence: "Elon Musk announced that Tesla will open a new factory in Germany by March 2026." A NER system should identify "Elon Musk" as a person, "Tesla" as an organization, "Germany" as a location, and "March 2026" as a date. This extracted structure powers an enormous range of downstream applications: search engines that understand queries about specific people, question-answering systems that can retrieve factual information, business intelligence pipelines that monitor mentions of competitors, and biomedical systems that extract drug-disease relationships from clinical notes.

NER combines sequence labeling with information extraction. Like Part-of-Speech (POS) tagging (covered in the previous chapter), NER assigns labels to tokens. But NER introduces a harder challenge: entities span multiple tokens, and detecting where an entity begins and ends is itself a non-trivial problem. This chapter covers the entity type taxonomy, how NER is formulated as a sequence labeling problem, the specific difficulty of boundary detection and nested entities, the long evolution of NER architectures from hand-written rules through CRFs to transformer-based models, and the major datasets and benchmarks that have shaped the field.