手法を比較

	Few-Shot Text Classification	BERT埋め込み
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	—	2019
提唱者	—	Devlin, Chang, Lee & Toutanova (Google AI)
種類	NLP text-classification task (low-resource)	Contextual transformer text-representation method
原典	Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
別名	few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot)	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.