Comparer des méthodes

	Modélisation thématique faiblement supervisée	Modèle thématique NMF
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2012–2017	1999
Auteur d'origine	Jagarlamudi, Daume & Udupa; Gallagher et al. (CorEx)	Lee, D. D. & Seung, H. S.
Type	Weakly supervised probabilistic topic model	Matrix factorization / unsupervised topic model
Source fondatrice	Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of EACL 2012, 204–213.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Alias	guided topic modeling, seed-guided topic model, constrained topic modeling, seeded LDA	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Apparentées	5	4
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.	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.