Entities & knowledge
Named Entity Recognition(NER)
The NLP task of finding entities in text and tagging each with its type.
Named Entity Recognition (NER) is the natural-language-processing task of scanning text, finding the entities in it, and tagging each with a type — company, person, place, product, date and so on. It is a building block engines use to understand what a passage is about and to connect its contents to known entities.
NER is relevant to content because clear, correctly named entities are easier to recognise and classify. Writing 'AnswerManiac, a GEO agency' gives an NER system an explicit entity and type; a vague pronoun or an ambiguous name gives it less to work with, weakening how well the passage is understood and connected.
The practical implication is to name entities explicitly and consistently, and to provide the context — category, type — that helps a system tag them correctly.
Related terms
Entity
A uniquely identifiable real-world thing an engine recognises, distinct from its name.
Entity Salience
A score for how central an entity is to a document as a whole.
Knowledge Graph
Google's structured database of real-world entities and the facts that connect them.
Semantic Triple
The atomic unit of linked data: one fact as subject, predicate, object.
Machine-Readable Content
Content encoded so software can parse its meaning, not just display its text.
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