Сравнение методов

	Адаптация к домену для распознавания именованных сущностей	Дообученное распознавание именованных сущностей
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2006–2020	2016–2019
Автор метода	Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)	Devlin, J. et al. (BERT fine-tuning paradigm); Lample, G. et al. (neural NER foundations)
Тип	Sequence labeling with domain adaptation	Supervised token classification via fine-tuned language model
Основополагающий источник	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. Proceedings of NAACL-HLT 2019, 4171–4186.
Другие названия	DA-NER, cross-domain NER, domain-adaptive NER, domain-transfer named entity recognition	Fine-tuned NER, BERT NER, transfer learning NER, neural NER with fine-tuning
Связанные	5	4
Сводка	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.	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.