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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>
하위 태스크
NN
Nested Named Entity Recognition
CN
Chinese Named Entity Recognition
FN
Few-shot NER
MN
Multi-modal Named Entity Recognition
ZN
Zero-shot Named Entity Recognition (NER)
MN
Medical Named Entity Recognition
N
NER
NE
Named Entity Recognition In Vietnamese
SC
Scientific Concept Extraction
CN
Cross-Domain Named Entity Recognition
MN
Multi-Grained Named Entity Recognition
MN
Multilingual Named Entity Recognition
TR
Toponym Recognition