en:tools:gibbstopicmodels
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| - | =====Topic Models（Gibbs Sampling）===== | ||
| - | ====Introduction==== | ||
| - | Topic model is family of generative probabilistic models for discovering the main themes from a collection of documents. For more elaborate and detailed surveys, we refer the readers to [1]. Examples of topic models include Latent Dirichlet Allocation (LDA) [2][3][4], Author-Topic (AT) model [5][6][7], and co-Author-Topic (coAT) model [8], and many others. | ||
| - | The inference for topic models usually cannot be done exactly. A variety of approximate inference algorithms have appeared in recent years, such as stochastic variational inference, mean-field variational methods, expectation propagation, and Monte Carlo Markov chain sampling (MCMC). In this toolbox, Gibbs sampling, a special case of MCMC, is utilized, since it provides a simple method for obtaining parameter estimates under Dirichlet priors and allows combination of estimates from several local maxima of the posterior distribution. | ||
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| - | ====Programming Language==== | ||
| - | JAVA | ||
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| - | ====Source Codes==== | ||
| - | https://github.com/pzczxs/GibbsTopicModels | ||
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| - | ====Citation Information==== | ||
| - | If you find this toolbox useful, please cite GibbsTopicModels as follows: | ||
| - | *Xin An, Shuo Xu, Yali Wen, and Mingxing Hu, 2014. [[http://dx.doi.org/10.1155/2014/820715|A Shared Interest Discovery Model for Coauthor Relationship in SNS]]. *International Journal of Distributed Sensor Networks*, Vol. 2014, No. 820715, pp. 1-9. ''{{xushuo:papers:axwh14.pdf|PDF}}'' | ||
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| - | ====References==== | ||
| - | -David M. Blei, 2012. [[http://dx.doi.org/10.1145/2133806.2133826|Introduction to Probabilistic Topic Models]]. //Communications of the ACM//, Vol. 55, No. 4, pp. 77-84. | ||
| - | -David M. Blei, Andrew Y. Ng, and Michael I. Jordan, 2003. [[http://jmlr.csail.mit.edu/papers/v3/blei03a.html|Latent Dirichlet Allocation]]. //Journal of Machine Learning Research//, Vol. 3, No. Jan, pp. 993-1022. | ||
| - | -Thomas L. Griffiths and Mark Steyvers, 2004. [[http://www.pnas.org/content/101/suppl_1/5228.abstract|Finding Scientific Topics]]. //Proceedings of the National Academy of Sciences of the United States of America//, Vol. 101, No. Suppl, pp. 5228-5235. | ||
| - | -Gregor Heinrich, 2009. [Parameter Estimation for Text Analysis](http://www.arbylon.net/publications/text-est2.pdf). *Technical Report Version 2.9*. vsonix GmbH and University of Leipzig. | ||
| - | -Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, and Padhraic Smyth, 2004. [[https://mimno.infosci.cornell.edu/info6150/readings/398.pdf|The Author-Topic Model for Authors and Documents]]. *Proceedings of the 20th International Conference on Uncertainty in Artificial Intelligence*, pp. 487-494. | ||
| - | -Mark Steyvers, Padhraic Smyth, and Thomas Griffiths, 2004. [[http://psiexp.ss.uci.edu/research/papers/author_topics_kdd.pdf|Probabilistic Author-Topic Models for Information Discovery]]. *Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining*, pp. 306-315. | ||
| - | -Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas Griffiths, and Padhraic Smyth, and Mark Steyvers, 2010. [[https://cocosci.berkeley.edu/tom/papers/AT_tois.pdf|Learning Author-Topic Models from Text Corpora]]. *ACM Transactions on Information Systems*, Vol. 28, No. 1, pp. 1-38. | ||
| - | -Xin An, Shuo Xu, Yali Wen, and Mingxing Hu, 2014. [[http://dx.doi.org/10.1155/2014/820715|A Shared Interest Discovery Model for Coauthor Relationship in SNS]]. *International Journal of Distributed Sensor Networks*, Vol. 2014, No. 820715, pp. 1-9. | ||
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| - | ~~DISCUSSION~~ | ||
en/tools/gibbstopicmodels.1492668013.txt.gz · 最后更改: 2017/04/20 14:00 由 pzczxs