قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| التعرف على الكيانات المسماة بالتعلم الذاتي× | تعلم العينات القليلة× | |
|---|---|---|
| المجال≠ | التعلم العميق | تعلم الآلة |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2018–2019 | 2011–2017 |
| صاحب الطريقة≠ | Devlin et al.; community-evolved from BERT-era self-supervised pretraining | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| النوع≠ | Sequence labeling via self-supervised pretraining + fine-tuning | Meta-learning / low-data learning paradigm |
| المصدر التأسيسي≠ | 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 ↗ |
| الأسماء البديلة | Self-supervised NER, SS-NER, label-efficient NER, pre-trained NER | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| ذات صلة≠ | 2 | 4 |
| الملخص≠ | 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. |
| ScholarGateمجموعة البيانات ↗ |
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