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Named Entity Recognition (NER)
Named Entity Recognition (NER) is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
Example:
| Mark | Watney | visited | Mars |
|---|---|---|---|
| B-PER | I-PER | O | B-LOC |
<span style="color:grey; opacity: 0.6">( Image credit: Zalando )</span>
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114개CoNLL 2003 (English)Ontonotes v5 (English)GENIANCBI-diseaseACE 2005ACE 2004WNUT 2017MSRAACE 2005Weibo NERJNLPBABC5CDROntoNotes 4GENIABC2GMBC5CDR-chemicalFew-NERD (INTER)Few-NERD (INTRA)Resume NERSLUECoNLL++BC5CDR-diseaseACE 2004BC4CHEMDSciERCWNUT 2016CoNLL 2002 (Dutch)CoNLL 2002 (Spanish)CoNLL 2003 (German)Few-NERD (SUP)