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Named Entity Recognition (NER) is the task of identifying and classifying named entities — specific real-world objects such as people, organisations, locations, dates, and more — within text. NER is a fundamental building block for information extraction, question answering, and knowledge graph construction.
| Entity Type | Tag | Examples |
|---|---|---|
| Person | PER / PERSON | "Albert Einstein", "Marie Curie" |
| Organisation | ORG | "Google", "United Nations", "Oxford University" |
| Location | LOC / GPE | "London", "Mount Everest", "France" |
| Date / Time | DATE / TIME | "14 March 2023", "next Monday" |
| Money | MONEY | "£50", "$1.2 million" |
| Percentage | PERCENT | "25%", "three quarters" |
| Product | PRODUCT | "iPhone", "Windows 11" |
| Event | EVENT | "World Cup", "COP26" |
Uses hand-crafted patterns and gazetteers (lists of known entities).
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