Methoden vergleichen

	Semi-supervised Topic Modeling	Latent Dirichlet Allocation (LDA)
Fachgebiet	Deep Learning	Maschinelles Lernen
Familie	Machine learning	Latent structure
Entstehungsjahr	2009	2003
Urheber	Ramage, D.; Andrzejewski, D.; and related NLP community	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
Typ	Probabilistic graphical model (supervised/constrained extension of LDA)	Generative probabilistic topic model (three-level hierarchical Bayesian)
Wegweisende Quelle	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Aliasnamen	semi-supervised LDA, labeled LDA, seed-guided topic modeling, constrained topic model	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
Verwandt	3	3
Zusammenfassung	Semi-supervised topic modeling extends unsupervised topic models such as LDA by incorporating partial human supervision — seed words, labeled documents, or must-link/cannot-link constraints — to steer discovered topics toward meaningful, domain-relevant categories while still exploiting the large unlabeled corpus for statistical strength.	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.