Sammenlign metoder

	Domene-adaptiv navngitt enhetsgjenkjenning	BERT-basert klassifisering
Fagfelt	Dyp læring	Dyp læring
Familie	Machine learning	Machine learning
Opprinnelsesår	2006–2020	2019
Opphavsperson	Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Type	Sequence labeling with domain adaptation	Pre-trained language model with fine-tuning
Opprinnelig kilde	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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
Alias	DA-NER, cross-domain NER, domain-adaptive NER, domain-transfer named entity recognition	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Relaterte	5	4
Sammendrag	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.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.