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NER locates and classifies named entities in unstructured text into predefined categories (e.g., PER, ORG, LOC, GPE).
Named-entity recognition (NER) is a subtask of information extraction that aims to locate and classify mentions of named entities in unstructured text into predefined categories. These categories can include person names (PER), organizations (ORG), locations (LOC), geopolitical entities (GPE), vehicles (VEH), and also other expression types such as time expressions, quantities, monetary values, and percentages. In practice, NER is often conceptualized as two related problems: (1) detection of entity mentions (typically treated as finding contiguous, non-nested spans of tokens), and (2) classification of each detected mention into an entity type using an ontology. The notion of “named entity” is not perfectly strict—some temporal or numerical expressions may be included or excluded depending on the task’s practical definition and goals.
NER locates and classifies named entities in unstructured text into predefined categories (e.g., PER, ORG, LOC, GPE).
NER is commonly split into entity detection (span identification) and entity classification (assigning an entity type via an ontology).
The definition of “named entity” is often loosened in practice, especially for time and numerical expressions, depending on task requirements.
A subtask of information extraction that finds and classifies mentions of named entities in unstructured text into predefined categories.
The task of identifying the spans of tokens that correspond to entity mentions, often modeled as contiguous, non-nested token spans.
The task of assigning each detected entity mention to a category or type (e.g., person, organization, location) according to an ontology.
A predefined set of entity types and categories used to organize and label the entities recognized by an NER system.
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