Markov Topic Models Export

@inproceedings{citeulike:6059970, abstract = {We develop Markov topic models (MTMs), a novel family of generative probabilistic models that can learn topics simultaneously from multiple corpora, such as papers from different conferences. We apply Gaussian (Markov) random fields to model the correlations of different corpora. MTMs capture both the internal topic structure within each corpus and the relationships between topics across the corpora. We derive an efficient estimation procedure with variational expectation-maximization. We study the performance of our models on a corpus of abstracts from six different computer science conferences. Our analysis reveals qualitative discoveries that are not possible with traditional topic models, and improved quantitative performance over the state of the art.}, author = {Wang, Chong and Thiesson, Bo and Meek, Christopher and Blei, David}, booktitle = {Proceedings of the 12th International Confe-rence on Artificial Intelligence and Statistics (AISTATS)}, citeulike-article-id = {6059970}, citeulike-linkout-0 = {http://www.cs.princeton.edu/~blei/papers/WangThiessonMeekBlei2009.pdf}, keywords = {topicmodels}, posted-at = {2009-11-03 11:43:14}, priority = {2}, title = {Markov Topic Models}, url = {http://www.cs.princeton.edu/~blei/papers/WangThiessonMeekBlei2009.pdf}, year = {2009} }

TY - CONF ID - citeulike:6059970 L3 - citeulike-article-id:6059970 N2 - We develop Markov topic models (MTMs), a novel family of generative probabilistic models that can learn topics simultaneously from multiple corpora, such as papers from different conferences. We apply Gaussian (Markov) random fields to model the correlations of different corpora. MTMs capture both the internal topic structure within each corpus and the relationships between topics across the corpora. We derive an efficient estimation procedure with variational expectation-maximization. We study the performance of our models on a corpus of abstracts from six different computer science conferences. Our analysis reveals qualitative discoveries that are not possible with traditional topic models, and improved quantitative performance over the state of the art. TI - Markov Topic Models T2 - Proceedings of the 12th International Confe-rence on Artificial Intelligence and Statistics (AISTATS) KW - topicmodels AU - Wang, Chong AU - Thiesson, Bo AU - Meek, Christopher AU - Blei, David PY - 2009/// UR - http://www.cs.princeton.edu/~blei/papers/WangThiessonMeekBlei2009.pdf ER -