方法对比

	主题建模	TF-IDF	Word2Vec
领域	文本挖掘	文本挖掘	文本挖掘
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	2003	1988	2013
提出者	Blei, Ng & Jordan	Salton & Buckley	Tomas Mikolov et al.
类型	Generative probabilistic topic model	Text vectorization / term-weighting scheme	Neural word-embedding model
开创性文献	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.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
别名	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
相关	4	3	4
摘要	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.	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.