方法对比

	弱监督LDA主题模型	半监督LDA主题模型
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2009–2012	2009
提出者	Jagarlamudi et al.; Andrzejewski et al.	Ramage, D.; Andrzejewski, D. et al.
类型	Probabilistic generative model with weak supervision	Semi-supervised probabilistic topic model
开创性文献	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.	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 EMNLP, 248–256.
别名	WS-LDA, Guided LDA, Seeded LDA, Constrained LDA	Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA
相关	6	6
摘要	Weakly Supervised LDA is an extension of Latent Dirichlet Allocation that incorporates lightweight human guidance — typically keyword seeds or must-link/cannot-link constraints — into the Dirichlet priors, steering learned topics toward domain-meaningful themes without requiring fully labeled documents. It sits between fully unsupervised LDA and supervised classification, making it well-suited to situations where labeling thousands of documents is impractical.	Semi-supervised LDA extends standard Latent Dirichlet Allocation by incorporating a small amount of supervision — seed words, labeled documents, or must-link/cannot-link word constraints — to guide topic discovery toward semantically coherent, interpretable themes. It bridges unsupervised topic modeling and fully supervised text classification, making it especially valuable when full annotation is costly.