Machine learningDeep learning / NLP / CV
弱监督LDA主题模型
弱监督LDA是潜在狄利克雷分配（Latent Dirichlet Allocation, LDA）的一个扩展，它将轻量级的人工指导——通常是关键词种子或必须链接/不能链接约束——纳入狄利克雷先验，引导学习到的主题朝向领域有意义的主题发展，而无需完全标注的文档。它介于完全无监督的LDA和有监督分类之间，非常适合标注数千份文档不切实际的情况。
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来源
- Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), pp. 204–213. link ↗
- Andrzejewski, D., Zhu, X., & Craven, M. (2009). Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), pp. 25–32. link ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Latent Dirichlet Allocation Topic Model. ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-lda-topic-model
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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