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| 자기 지도 학습 기반 명명 개체 인식× | 개체명 인식 (NER)× | |
|---|---|---|
| 분야≠ | 딥러닝 | 텍스트 마이닝 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 2018–2019 | — |
| 창시자≠ | Devlin et al.; community-evolved from BERT-era self-supervised pretraining | — |
| 유형≠ | Sequence labeling via self-supervised pretraining + fine-tuning | NLP sequence-labelling task |
| 원전≠ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186. link ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| 별칭≠ | Self-supervised NER, SS-NER, label-efficient NER, pre-trained NER | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| 관련≠ | 2 | 3 |
| 요약≠ | Self-supervised named entity recognition (NER) combines large-scale self-supervised pretraining — such as masked language modeling — with token-level fine-tuning to identify and classify named entities in text. By learning general linguistic representations before seeing any entity labels, the model achieves strong performance even when annotated NER training data is scarce. | Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use. |
| ScholarGate데이터셋 ↗ |
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