Named-Entity Recognition Learning Path

Definition and scope of named entities

In named-entity recognition (NER), the term “named entity” restricts the task to entities that are consistently referred to by one or more strings (words or phrases). This is closely related to the philosophical idea of rigid designators, but in practice NER is applied to many names and referents that are not strictly rigid. NER typically focuses on proper names and other referential expressions (e.g., certain species/substances), while excluding pronouns and descriptive phrases that identify referents by properties, as well as names for kinds of things rather than individuals. The scope of NER is often operationalized by splitting the problem into two parts: (1) detection of entity name spans and (2) classification of those spans into an entity type using an ontology (e.g., person, organization, location). Detection is commonly treated as a segmentation/chunking problem where entity mentions are contiguous, non-nested token spans (e.g., “Bank of America” as one span). Classification may also include temporal and numerical expressions (years, money, percentages), though the “named entity” definition becomes looser for practical reasons because some such expressions can be ambiguous in scope (e.g., “June” without a specified year).

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Detection versus classification in NER

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