So sánh phương pháp

	Nhận dạng thực thể có tên thích ứng miền	Nhận dạng thực thể có tên (NER)
Lĩnh vực	Học sâu	Khai phá văn bản
Họ	Machine learning	Process / pipeline
Năm ra đời	2006–2020	—
Người khởi xướng	Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)	—
Loại	Sequence labeling with domain adaptation	NLP sequence-labelling task
Công trình gốc	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.
Tên gọi khác	DA-NER, cross-domain NER, domain-adaptive NER, domain-transfer named entity recognition	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Liên quan	5	3
Tóm tắt	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.