Machine learningDeep learning / NLP / CV

命名实体识别的迁移学习

命名实体识别（NER）的迁移学习将大型预训练语言模型（如BERT、RoBERTa或领域专用编码器）应用于识别和分类文本中的命名实体（人名、地点、组织、日期等）的任务。通过重用从海量语料库中学习到的丰富语言表示，该方法仅需适量的标注NER数据，即可实现最先进的跨度检测和分类准确性。

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命名实体识别的迁移学习

[需翻译标题：BERT-based Classi…微调命名实体识别基于RoBERTa的分类句子嵌入 BERT 기반 전이 학습 迁移学习与文本摘要

来源

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics. DOI: 10.18653/v1/N19-1423 ↗
Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗

如何引用本页

ScholarGate. (2026, June 3). Transfer Learning with Named Entity Recognition (Pretrained Encoder Fine-Tuned for NER). ScholarGate. https://scholargate.app/zh/deep-learning/transfer-learning-with-named-entity-recognition

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

[需翻译标题：BERT-based Classification...]深度学习↔ compare
微调命名实体识别深度学习↔ compare
基于RoBERTa的分类深度学习↔ compare
句子嵌入深度学习↔ compare
BERT 기반 전이 학습深度学习↔ compare

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迁移学习与文本摘要

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