Comparer des méthodes

	Modélisation thématique semi-supervisée	Word2Vec
Domaine	Apprentissage profond	Fouille de textes
Famille	Machine learning	Process / pipeline
Année d'origine	2009	2013
Auteur d'origine	Ramage, D.; Andrzejewski, D.; and related NLP community	Tomas Mikolov et al.
Type	Probabilistic graphical model (supervised/constrained extension of LDA)	Neural word-embedding model
Source fondatrice	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.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Alias	semi-supervised LDA, labeled LDA, seed-guided topic modeling, constrained topic model	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Apparentées	3	4
Résumé	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.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.