Machine learningDeep learning / NLP / CV
自监督命名实体识别
自监督命名实体识别（NER）将大规模自监督预训练（如掩码语言模型）与令牌级微调相结合，以识别和分类文本中的命名实体。通过在看到任何实体标签之前学习通用的语言表征，即使在标注的 NER 训练数据稀缺的情况下，模型也能取得优异的性能。
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来源
- 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 ↗
- Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition. Proceedings of NAACL-HLT 2016, 260–270. link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Named Entity Recognition. ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-named-entity-recognition
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