Named Entity Recognition
AI technique that identifies and classifies named entities like people, organizations, and locations in text.
What it does and why it matters
Named entity recognition (NER) finds the important nouns in text and labels them. It scans a document and tags every person name, company, location, date, monetary value, and other specific entities. "Apple announced iPhone 16 in Cupertino on September 9th" becomes Apple=COMPANY, iPhone 16=PRODUCT, Cupertino=LOCATION, September 9th=DATE. The AI automatically extracts structured data from unstructured text.
This matters because text is messy. Contracts mention dozens of parties and dates. News articles reference multiple companies and people. Medical records contain drug names, dosages, and symptoms. Manually extracting all these entities is tedious and error-prone. NER automates it, letting you build databases from documents, power search systems, and connect information across sources.
The technology typically uses neural networks trained on annotated text. Models learn patterns like "Inc." and "Corp." following company names, or capitalized words in certain positions being person names. Modern NER handles edge cases pretty well. "Apple" the company vs "apple" the fruit. "Washington" the person vs "Washington" the city. Context helps disambiguate.
Real applications are widespread. Law firms extract parties and dates from contracts. News organizations tag articles for better search and recommendations. Financial companies pull company names and figures from earnings reports. Healthcare systems identify medications and conditions in clinical notes. Any workflow that involves reading documents and recording specific facts benefits from NER.