Porównaj metody

	Wyjaśnialny model tematyczny LDA	Word2Vec
Dziedzina	Uczenie głębokie	Eksploracja tekstu
Rodzina	Machine learning	Process / pipeline
Rok powstania	2003 (LDA); 2018–present (explainability extensions)	2013
Twórca	Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors	Tomas Mikolov et al.
Typ	Probabilistic generative topic model with interpretability enhancements	Neural word-embedding model
Źródło pierwotne	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.
Inne nazwy	Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Pokrewne	4	4
Podsumowanie	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.