手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| Few-Shot Text Classification× | ドメイン適応× | |
|---|---|---|
| 分野 | テキストマイニング | テキストマイニング |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年 | — | — |
| 提唱者 | — | — |
| 種類≠ | NLP text-classification task (low-resource) | NLP transfer-learning / fine-tuning pipeline |
| 原典≠ | Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL. DOI ↗ | Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics. DOI ↗ |
| 別名≠ | few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot) | Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning |
| 関連 | 4 | 4 |
| 概要≠ | 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. |
| ScholarGateデータセット ↗ |
|
|