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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.
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