Comparer des méthodes

	Modèle de Topics LDA Faiblement Supervisé	Modèle de Topics LDA
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2009–2012	2003
Auteur d'origine	Jagarlamudi et al.; Andrzejewski et al.	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Type	Probabilistic generative model with weak supervision	Probabilistic generative topic model
Source fondatrice	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	WS-LDA, Guided LDA, Seeded LDA, Constrained LDA	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Apparentées	6	5
Résumé	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.	Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.