@article {blei_latent_2003,
	title = {Latent Dirichlet Allocation},
	journal = {J. Mach. Learn. Res.},
	volume = {3},
	year = {2003},
	note = {10035},
	pages = {993{\textendash}1022},
	abstract = {In this highly technical article written by computer science scholars, Blei, Ng, and Jordan discuss topic modelling textual corpora through LDA (latent Dirichlet allocation). This article begins by defining LDA and then moves through the steps necessary to carry out this type of analysis. Blei, Ng, and Jordan help their audience understand the common problems with LDA and purpose troubleshooting solutions. The article concludes with an exemplar analysis, complete with illustrative figures.  },
	issn = {1532-4435},
	url = {http://dl.acm.org/citation.cfm?id=944919.944937},
	author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.}
}