Entity extraction is a natural language processing technique that identifies and classifies specific pieces of information within unstructured text, such as names of people, organizations, locations, dates, and monetary amounts, and labels them according to predefined categories. It's a foundational step for turning free-flowing text into structured data a machine can systematically work with.
This matters at scale for any task that needs to pull specific facts out of large volumes of text automatically: legal teams use entity extraction to flag names and dates across contracts, news organizations use it to tag people and organizations across articles, and customer support systems use it to pull order numbers or product names out of incoming messages, all without requiring a human to read and tag every document manually.
