Named Entity Recognition
The NLP task of identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, dates, monetary values, and more.
Why It Matters
NER is a foundational NLP capability that powers information extraction, document processing, search indexing, and compliance monitoring across industries.
Example
Extracting from a news article: [Apple] = Organization, [Tim Cook] = Person, [Cupertino] = Location, [September 2025] = Date, [$3 trillion] = Money.
Think of it like...
Like a highlighter system that automatically color-codes different types of information in a document — names in yellow, places in blue, dates in green.
Related Terms
Natural Language Processing
The branch of AI that deals with the interaction between computers and human language. NLP enables machines to read, understand, generate, and make sense of human language in a useful way.
Information Extraction
The task of automatically extracting structured information (entities, relationships, events) from unstructured text documents.
Text Mining
The process of deriving meaningful patterns, trends, and insights from large collections of text data using NLP and statistical techniques.
Tokenization
The process of breaking text into smaller units (tokens) for processing by NLP models. Tokenization can split text into words, subwords, or characters depending on the method used.