Comparer des méthodes

	Modélisation thématique faiblement supervisée	Modélisation par sujets
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2012–2017	1999–2003
Auteur d'origine	Jagarlamudi, Daume & Udupa; Gallagher et al. (CorEx)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Type	Weakly supervised probabilistic topic model	Unsupervised generative probabilistic model
Source fondatrice	Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of EACL 2012, 204–213.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	guided topic modeling, seed-guided topic model, constrained topic modeling, seeded LDA	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Apparentées	5	5
Résumé	Weakly supervised topic modeling incorporates lightweight domain knowledge — typically seed words or soft constraints — into a probabilistic topic model to steer discovered topics toward researcher-meaningful themes. It sits between fully unsupervised LDA and supervised classifiers, requiring far less annotation than the latter while producing more interpretable and domain-aligned topics than the former.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.