Salīdzināt metodes

	Tēmu modelēšana	TF-IDF
Nozare	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2003	1988
Autors	Blei, Ng & Jordan	Salton & Buckley
Tips	Generative probabilistic topic model	Text vectorization / term-weighting scheme
Pirmavots	Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Citi nosaukumi	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Saistītās	4	3
Kopsavilkums	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.