Comparar métodos

	Reconhecimento de Entidades Nomeadas Adaptativo ao Domínio	Fine-Tuned Named Entity Recognition
Área	Aprendizado profundo	Aprendizado profundo
Família	Machine learning	Machine learning
Ano de origem	2006–2020	2016–2019
Autor original	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)
Tipo	Sequence labeling with domain adaptation	Supervised token classification via fine-tuned language model
Fonte seminal	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.
Outros nomes	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
Relacionados	5	4
Resumo	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.