Comparer des méthodes

	Modèle de Topics LDA Explicable	Word2Vec
Domaine	Apprentissage profond	Fouille de textes
Famille	Machine learning	Process / pipeline
Année d'origine	2003 (LDA); 2018–present (explainability extensions)	2013
Auteur d'origine	Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors	Tomas Mikolov et al.
Type	Probabilistic generative topic model with interpretability enhancements	Neural word-embedding model
Source fondatrice	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Alias	Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Apparentées	4	4
Résumé	Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science text analysis, and computational humanities where transparency is required alongside discovery.	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.