Comparar métodos

	Classificação de Textos com Poucos Exemplos	Adaptação de Domínio
Área	Mineração de texto	Mineração de texto
Família	Process / pipeline	Process / pipeline
Ano de origem	—	—
Autor original	—	—
Tipo	NLP text-classification task (low-resource)	NLP transfer-learning / fine-tuning pipeline
Fonte seminal	Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL.	Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics.
Outros nomes	few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot)	Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning
Relacionados	4	4
Resumo	Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels.	Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model.