Machine learningDeep learning / NLP / CV
弱监督BERT分类
弱监督BERT分类将BERT应用于文本分类任务，此时仅有噪声、启发式或程序化生成的标签可用，而非干净的人工标注。它结合了弱监督框架（如标注函数和数据编程）与BERT的预训练语言表示，以在没有昂贵人工标注的情况下实现鲁棒的分类。
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来源
- Meng, Y., Zhang, Y., Huang, J., Xiong, C., Ji, H., Zhang, C., & Han, J. (2020). Text Classification Using Label Names Only: A Language Model Self-Training Approach. Proceedings of EMNLP 2020, 9006–9017. link ↗
- Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid Training Data Creation with Weak Supervision. Proceedings of the VLDB Endowment, 11(3), 269–282. DOI: 10.14778/3157794.3157797 ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised BERT-based Text Classification. ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-bert-based-classification
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.
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