Σύγκριση μεθόδων

	Θεματική Μοντελοποίηση	Word2Vec
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2003	2013
Δημιουργός	Blei, Ng & Jordan	Tomas Mikolov et al.
Τύπος	Generative probabilistic topic model	Neural word-embedding model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Συναφείς	4	4
Σύνοψη	Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpus into interpretable themes.	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.