Machine learningDeep learning / NLP / CV
半监督主题建模
半监督主题建模扩展了LDA等无监督主题模型，通过整合部分人工监督——种子词、标记文档或必须链接/不能链接约束——来引导发现的主题朝向有意义的、领域相关的类别，同时仍利用大量未标记语料库的统计强度。
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来源
- Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 248–256. Association for Computational Linguistics. link ↗
- Andrzejewski, D., Zhu, X., & Craven, M. (2009). Incorporating domain knowledge into topic modeling via Dirichlet forest priors. Proceedings of the 26th Annual International Conference on Machine Learning (ICML), 25–32. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Topic Modeling (Seed-guided and Labeled LDA variants). ScholarGate. https://scholargate.app/zh/deep-learning/semi-supervised-topic-modeling
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