Named Entity Recognition (NER)
Labeling spans of text as people, organizations, locations, or other defined entity types.
Named Entity Recognition tags specific spans within a text — not whole documents or sentences — as instances of predefined categories like PERSON, ORGANIZATION, LOCATION, or DATE. Unlike document classification, which labels a whole piece of text once, NER requires finding and bounding every relevant mention within it.
NER is foundational infrastructure for a lot of downstream NLP: it's often the first pass before entity linking (mapping a mention to a specific real-world entity) or relation extraction (labeling how two entities relate to each other).
Ambiguity in NER usually comes from entity boundaries (does a title count as part of a person's name?) and overlapping categories (is "Amazon" a location, an organization, or both, depending on context) — both resolved through clear guidelines.
What this means for trainers
Boundary precision matters as much as category choice — get the span exactly right, not just approximately right, since downstream systems depend on exact character offsets.
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