Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pašuzraudzītā nosaukto entitāšu atpazīšana× | Mācīšanās ar maziem paraugu skaitu× | |
|---|---|---|
| Nozare≠ | Dziļā mācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2018–2019 | 2011–2017 |
| Autors≠ | Devlin et al.; community-evolved from BERT-era self-supervised pretraining | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Tips≠ | Sequence labeling via self-supervised pretraining + fine-tuning | Meta-learning / low-data learning paradigm |
| Pirmavots≠ | 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. link ↗ | 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. link ↗ |
| Citi nosaukumi | Self-supervised NER, SS-NER, label-efficient NER, pre-trained NER | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Saistītās≠ | 2 | 4 |
| Kopsavilkums≠ | 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. |
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