So sánh phương pháp

	Phân bổ Dirichlet ẩn (LDA)	Phân tích ma trận không âm (NMF)	Word2Vec
Lĩnh vực	Học máy	Học máy	Khai phá văn bản
Họ	Latent structure	Latent structure	Process / pipeline
Năm ra đời	2003	1999	2013
Người khởi xướng	Blei, D. M.; Ng, A. Y.; Jordan, M. I.	Lee, D. D. & Seung, H. S.	Tomas Mikolov et al.
Loại	Generative probabilistic topic model (three-level hierarchical Bayesian)	Matrix decomposition with non-negativity constraints	Neural word-embedding model
Công trình gốc	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Tên gọi khác	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Liên quan	3	4	4
Tóm tắt	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.	Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.	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.