手法を比較

	ドメイン適応型固有表現認識	固有表現抽出（NER）
分野	深層学習	テキストマイニング
系統	Machine learning	Process / pipeline
提唱年	2006–2020	—
提唱者	Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)	—
種類	Sequence labeling with domain adaptation	NLP sequence-labelling task
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.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
別名	DA-NER, cross-domain NER, domain-adaptive NER, domain-transfer named entity recognition	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
関連	5	3
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.	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.