手法を比較

	ドメイン適応型固有表現認識	ドメイン適応型BERTベース分類
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2006–2020	2019–2020
提唱者	Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)	Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT
種類	Sequence labeling with domain adaptation	Domain-adaptive pre-training followed by supervised fine-tuning
原典	Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.	Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360.
別名	DA-NER, cross-domain NER, domain-adaptive NER, domain-transfer named entity recognition	DAPT BERT classification, domain-adaptive pre-training, domain-specific BERT fine-tuning, BERT DAPT
関連	5	6
概要	Domain-adaptive Named Entity Recognition (DA-NER) applies named entity recognition to a target domain by transferring or adapting a model trained on a source domain, using techniques such as domain-specific pre-training, adversarial alignment, or feature augmentation. It addresses the performance collapse that standard NER models suffer when deployed outside their training domain.	Domain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.