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| 미세 조정된 개체명 인식× | RoBERTa 기반 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2016–2019 | 2019 |
| 창시자≠ | Devlin, J. et al. (BERT fine-tuning paradigm); Lample, G. et al. (neural NER foundations) | Liu, Y. et al. (Facebook AI Research / University of Washington) |
| 유형≠ | Supervised token classification via fine-tuned language model | Pre-trained transformer fine-tuned for sequence classification |
| 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. DOI ↗ | Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692. link ↗ |
| 별칭 | Fine-tuned NER, BERT NER, transfer learning NER, neural NER with fine-tuning | RoBERTa classifier, RoBERTa text classification, Robustly Optimized BERT Classification, RoBERTa fine-tuning for classification |
| 관련≠ | 4 | 5 |
| 요약≠ | Fine-Tuned Named Entity Recognition adapts a pre-trained language model — most commonly BERT or one of its derivatives — to the task of identifying and classifying named entities (persons, organizations, locations, dates, etc.) in text. By fine-tuning on a relatively small labeled corpus, practitioners achieve state-of-the-art sequence-labeling performance without training a model from scratch. | RoBERTa-based Classification applies the RoBERTa pre-trained transformer — trained more robustly than BERT with dynamic masking and larger batches — to text categorisation tasks by adding a lightweight classification head on top of the [CLS] token representation and fine-tuning the entire model on labelled examples. It consistently matches or outperforms BERT on standard NLP benchmarks. |
| ScholarGate데이터셋 ↗ |
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