Bandingkan kaedah

	Pengenalan Entiti Bernama Kendiri-Penyeliaan	Pembelajaran Sifar Contoh (Few-shot Learning)
Bidang	Pembelajaran Mendalam	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2018–2019	2011–2017
Pengasas	Devlin et al.; community-evolved from BERT-era self-supervised pretraining	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Jenis	Sequence labeling via self-supervised pretraining + fine-tuning	Meta-learning / low-data learning paradigm
Sumber perintis	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.	Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.
Alias	Self-supervised NER, SS-NER, label-efficient NER, pre-trained NER	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Berkaitan	2	4
Ringkasan	Self-supervised named entity recognition (NER) combines large-scale self-supervised pretraining — such as masked language modeling — with token-level fine-tuning to identify and classify named entities in text. By learning general linguistic representations before seeing any entity labels, the model achieves strong performance even when annotated NER training data is scarce.	Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.