1. Landauer TK. Landauer TK, McNamara DS, Dennis S, Kintsch W, editors. LSA as a theory of meaning. Handbook of latent semantic analysis. 2007. Mahwah (NJ): Lawrence Erlbaum Associates;3–35.
2. Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R. Indexing by latent semantic analysis. J Am Soc Inf Sci. 1990. 41:391–407.
Article
3. Dumais ST. McNamara DS, Dennis S, Kintsch W, editors. LSA and information retrieval: back to basics. Handbook of latent semantic analysis. 2007. Mahwah (NJ): Lawrence Erlbaum Associates;293–321.
4. Hofmann T. Probabilistic latent semantic indexing. 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1999. 1999 Aug 15-19; Berkeley, CA. New York: Association for Computing Machinery;50–57.
Article
5. Hofmann T. Unsupervised learning by probabilistic latent semantic analysis. Mach Learn. 2001. 42:177–196.
6. Blei D, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003. 3:993–1022.
7. Steyvers M, Smyth P, Rosen-Zvi M, Griffiths TL. Probabilistic author-topic models for information discovery. In : Knowledge Discovery and Data Mining 2004; 2004 August 22-25; Seattle, WA. 306–315.
8. Wang X, McCallum A. Topics over time: a non-Markov continuous-time model of topical trends. 2006. In : Knowledge Discovery and Data Mining 2006; 2006 August 20-23; Philadelphia, PA. New York: Association for Computing Machinery;–.
9. Blei DM, Lafferty JD. Dynamic topic models. In : The 23rd International Conference of Machine Learning; 2006 June 25-29; Pittsburgh, PA.
10. Manning CD, Raghavan P, Schütze H. Introduction to information retrieval. 2008. 1st ed. New York: Cambridge University Press;378–384.