Techniques & Methods
Named Entity Recognition (NER)
NER identifies spans of text that refer to named entities and classifies them into categories such as PERSON, ORGANIZATION, LOCATION, DATE, PRODUCT, and EVENT. It is a foundational NLP task used in information extraction, knowledge graph construction, and search.
Modern NER systems use fine-tuned transformer models (BERT-based or LLM-based) that achieve near-human accuracy. NER is often the first step in building structured knowledge from unstructured text at scale.
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Related Terms
Core Concepts
Entities
Specific, identifiable elements like names, places, and dates extracted from text.
Techniques & Methods
Entity Extraction
Identifying and classifying named entities—people, places, organizations—within text.
Techniques & Methods
Information Extraction
Automatically extracting structured information from unstructured text.
Techniques & Methods
Part-of-Speech Tagging (POS)
Labeling each word in text with its grammatical role such as noun, verb, or adjective.