Sat, 05 Jul 2008 22:47:54 BST CiteULike: mshafiei's topic-modeling http://www.citeulike.org/user/mshafiei/tag/topic-modeling CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/mshafiei/article/2799734 <i>Statistical Network Analysis: Models, Issues, and New Directions (2007), pp. 28-44.</i><br /><br />We present a probabilistic generative model of entity relationships and textual attributes; the model simultaneously discovers groups among the entities and topics among the corresponding text. Block models of relationship data have been studied in social network analysis for some time, however here we cluster in multiple modalities at once. Significantly, joint inference allows the discovery of groups to be guided by the emerging topics, and vice-versa. We present experimental results on two large data sets: sixteen years of bills put before the U.S. Senate, comprising their corresponding text and voting records, and 43 years of similar data from the United Nations. We show that in comparison with traditional, separate latent-variable models for words or block structures for votes, our Group-Topic model’s joint inference improves both the groups and topics discovered. Additionally, we present a non-Markov continouous-time group model to capture shifting group structure over time. Joint Group and Topic Discovery from Relations and Text Andrew Mccallum Xuerui Wang Natasha Mohanty doi:10.1007/978-3-540-73133-7_3 Statistical Network Analysis: Models, Issues, and New Directions (2007), pp. 28-44. 2008-05-14T18:32:45-00:00 2007 Statistical Network Analysis: Models, Issues, and New Directions 28 44 bayesian network-analysis text topic-modeling http://www.citeulike.org/user/mshafiei/article/462772 <i>(2004), pp. 122-129.</i> GaP: a factor model for discrete data John Canny doi:10.1145/1008992.1009016 (2004), pp. 122-129. 2006-01-12T09:19:38-00:00 2004 122 129 ACM Press topic-modeling http://www.citeulike.org/user/mshafiei/article/821915 <i>(2006), pp. 680-686.</i> Statistical entity-topic models David Newman Chaitanya Chemudugunta Padhraic Smyth doi:10.1145/1150402.1150487 (2006), pp. 680-686. 2006-08-30T10:42:33-00:00 2006 680 686 ACM Press topic-modeling http://www.citeulike.org/user/mshafiei/article/698679 <i>Proc Natl Acad Sci U S A, Vol. 101 Suppl 1 (6 April 2004), pp. 5220-5227.</i><br /><br />PNAS is one of world's most cited multidisciplinary scientific journals. The PNAS official classification structure of subjects is reflected in topic labels submitted by the authors of articles, largely related to traditionally established disciplines. These include broad field classifications into physical sciences, biological sciences, social sciences, and further subtopic classifications within the fields. Focusing on biological sciences, we explore an internal soft-classification structure of articles based only on semantic decompositions of abstracts and bibliographies and compare it with the formal discipline classifications. Our model assumes that there is a fixed number of internal categories, each characterized by multinomial distributions over words (in abstracts) and references (in bibliographies). Soft classification for each article is based on proportions of the article's content coming from each category. We discuss the appropriateness of the model for the PNAS database as well as other features of the data relevant to soft classification. Mixed-membership models of scientific publications. E Erosheva S Fienberg J Lafferty doi:10.1073/pnas.0307760101 Proc Natl Acad Sci U S A, Vol. 101 Suppl 1 (6 April 2004), pp. 5220-5227. 2006-06-16T19:49:08-00:00 2004 Proc Natl Acad Sci U S A 0027-8424 101 Suppl 1 5220 5227 citation-graph relational topic-modeling http://www.citeulike.org/user/mshafiei/article/2462439 <i>(2006), pp. 55-64.</i> Discovering groups of people in Google news Dhiraj Joshi Daniel Gatica-Perez doi:10.1145/1178745.1178757 (2006), pp. 55-64. 2008-03-03T23:09:18-00:00 2006 55 64 ACM sna social-network-analysis topic-modeling http://www.citeulike.org/user/mshafiei/article/2462399 <i>Proceedings of the 20th conference on Uncertainty in artificial intelligence (2004), pp. 487-494.</i> The author-topic model for authors and documents Rosen Zvi T Griffiths M Steyvers P Smyth Proceedings of the 20th conference on Uncertainty in artificial intelligence (2004), pp. 487-494. 2008-03-03T22:56:22-00:00 2004 Proceedings of the 20th conference on Uncertainty in artificial intelligence 487 494 AUAI Press Arlington, Virginia, United States topic-modeling http://www.citeulike.org/user/mshafiei/article/2462356 <i>(2007), pp. 241-248.</i> Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model Chaitanya Chemudugunta Padhraic Smyth Mark Steyvers (2007), pp. 241-248. 2008-03-03T22:38:06-00:00 2007 241 248 MIT Press topic-modeling http://www.citeulike.org/user/mshafiei/article/2460959 <i>(2007)</i> HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation Bing Zhao Eric Xing (2007) 2008-03-03T14:33:11-00:00 2007 topic-modeling translation variational http://www.citeulike.org/user/mshafiei/article/1026212 <i>(2006), pp. 907-914.</i> Context as Filtering Daichi Mochihashi Yuji Matsumoto (2006), pp. 907-914. 2007-01-05T02:54:01-00:00 2006 907 914 MIT Press bayesian bayesian-inference language-model language-modeling nlp topic-modeling http://www.citeulike.org/user/mshafiei/article/1853878 <i>(2007), pp. 370-379.</i> Detecting research topics via the correlation between graphs and texts Yookyung Jo Carl Lagoze Lee Giles doi:10.1145/1281192.1281234 (2007), pp. 370-379. 2007-11-02T01:59:42-00:00 2007 370 379 ACM link-content network-analysis topic-modeling http://www.citeulike.org/user/mshafiei/article/2430950 <i>(2007)</i> Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning Arindam Banerjee Sugato Basu (2007) 2008-02-26T19:04:26-00:00 2007 SIAM topic-modeling http://www.citeulike.org/user/mshafiei/article/2430872 <i>(2007), pp. 633-640.</i> Mixtures of hierarchical topics with Pachinko allocation David Mimno Wei Li Andrew Mccallum doi:10.1145/1273496.1273576 (2007), pp. 633-640. 2008-02-26T18:48:33-00:00 2007 633 640 ACM bayesian topic-modeling http://www.citeulike.org/user/mshafiei/article/2430059 <i>(2004), pp. 113-120.</i> Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization Regina Barzilay Lillian Lee (2004), pp. 113-120. 2008-02-26T15:42:32-00:00 2004 113 120 topic-modeling http://www.citeulike.org/user/mshafiei/article/2430058 <i>Vol. 2777 (2003), pp. 173-187.</i> Comparing Clusterings by the Variation of Information. Marina Meila Vol. 2777 (2003), pp. 173-187. 2008-02-26T15:42:31-00:00 2003 2777 173 187 Springer topic-modeling http://www.citeulike.org/user/mshafiei/article/2430057 <i>(2004), pp. 113-120.</i> Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization. Regina Barzilay Lillian Lee (2004), pp. 113-120. 2008-02-26T15:42:30-00:00 2004 113 120 topic-modeling