Anchors regularized: Adding robustness and extensibility to scalable topic-modeling algorithms

52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference

Volumn 1, Issue , 2014, Pages 359-369

  Nguyen, Thang ^a   Hu, Yuening ^b   Boyd Graber, Jordan ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS;

DIRICHLET PRIOR; EXPECTATION - MAXIMIZATIONS; INTERPRETABILITY; MARKOV CHAIN MONTE-CARLO; PROBABILISTIC MODELS; REGULARIZATION APPROACH; ROBUSTNESS AND EXTENSIBILITY; SPECTRAL METHODS;

ALGORITHMS;

EID: 84906928276     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (39)

1. Animashree Anandkumar, Daniel Hsu, and Sham M. Kakade. 2012. A method of moments for mixture models and hidden markov models. In Proceedings of Conference on Learning Theory.
2. David Andrzejewski, Xiaojin Zhu, and Mark Craven. 2009. Incorporating domain knowledge into topic modeling via Dirichlet forest priors. In Proceedings of the International Conference of Machine Learning.
3. Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David Sontag, Yichen Wu, and Michael Zhu. 2012a. A practical algorithm for topic modeling with provable guarantees. CoRR, abs/1212.4777.
4. Sanjeev Arora, Rong Ge, and Ankur Moitra. 2012b. Learning topic models - going beyond svd. CoRR, abs/1204.1956.
5. David M. Blei and John D. Lafferty. 2005. Correlated topic models. In Proceedings of Advances in Neural Information Processing Systems.
6. David M. Blei, Andrew Ng, and Michael Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3.
7. Samuel Brody and Mirella Lapata. 2009. Bayesian word sense induction. In Proceedings of the European Chapter of the Association for Computational Linguistics, Athens, Greece.
8. Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M. Blei. 2009. Reading tea leaves: How humans interpret topic models. In Proceedings of Advances in Neural Information Processing Systems.
9. Shay B. Cohen and Noah A. Smith. 2009. Shared logistic normal distributions for soft parameter tying in unsupervised grammar induction. In Conference of the North American Chapter of the Association for Computational Linguistics.
10. Shay Cohen, Karl Stratos, Michael Collins, Dean P. Foster, and Lyle Ungar. 2013. Experiments with spectral learning of latent-variable PCFGs. In Conference of the North American Chapter of the Association for Computational Linguistics.
11. Hal Daumé III. 2007. Frustratingly easy domain adaptation. In Proceedings of the Association for Computational Linguistics.
12. David Donoho and Victoria Stodden. 2003. When does non-negative matrix factorization give correct decomposition into parts? page 2004. MIT Press.
13. Miroslav Dudík, Steven J. Phillips, and Robert E. Schapire. 2004. Performance guarantees for regularized maximum entropy density estimation. In Proceedings of Conference on Learning Theory.
14. Vladimir Eidelman, Jordan Boyd-Graber, and Philip Resnik. 2012. Topic models for dynamic translation model adaptation. In Proceedings of the Association for Computational Linguistics.
15. Jenny Rose Finkel and Christopher D. Manning. 2009. Hierarchical bayesian domain adaptation. In Conference of the North American Chapter of the Association for Computational Linguistics, Morristown, NJ, USA.
16. Mark Galassi, Jim Davies, James Theiler, Brian Gough, Gerard Jungman, Michael Booth, and Fabrice Rossi. 2003. Gnu Scientific Library: Reference Manual. Network Theory Ltd.
17. Thomas L. Griffiths and Mark Steyvers. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences, 101(Suppl 1):5228-5235.
18. David Hall, Daniel Jurafsky, and Christopher D. Manning. 2008. Studying the history of ideas using topic models. In Proceedings of Emperical Methods in Natural Language Processing.
19. Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. 2013. Interactive topic modeling. Machine Learning Journal.
20. Matt L. Jockers. 2013. Macroanalysis: Digital Methods and Literary History. Topics in the Digital Humanities. University of Illinois Press.
21. Ravindran Kannan, Hadi Salmasian, and Santosh Vempala. 2005. The spectral method for general mixture models. In Proceedings of Conference on Learning Theory.
22. Ken Lang. 2007. 20 newsgroups data set.
23. Jey Han Lau, David Newman, and Timothy Baldwin. 2014. Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality. In Proceedings of the European Chapter of the Association for Computational Linguistics.
24. Andrew Kachites McCallum. 2002. Mallet: A machine learning for language toolkit. http://www.cs.umass.edu/ mccallum/mallet.
25. Thomas P. Minka. 2000. Estimating a dirichlet distribution. Technical report, Microsoft. http://research.microsoft.com/enus/ um/people/minka/papers/ dirichlet/.
26. David Newman, Jey Han Lau, Karl Grieser, and Timothy Baldwin. 2010. Automatic evaluation of topic coherence. In Conference of the North American Chapter of the Association for Computational Linguistics.
27. Andrew Y. Ng. 2004. Feature selection, l1 vs. l2 regularization, and rotational invariance. In Proceedings of the International Conference of Machine Learning.
28. Michael Paul and Roxana Girju. 2010. A twodimensional topic-aspect model for discovering multi-faceted topics. In Association for the Advancement of Artificial Intelligence.
29. James W. Pennebaker and Martha E. Francis. 1999. Linguistic Inquiry and Word Count. Lawrence Erlbaum, 1 edition, August.
30. Lawrence R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286.
31. Jason Rennie. 2003. On l2-norm regularization and the Gaussian prior.
32. Sam Roweis. 2002. NIPS 1-12 Dataset.
33. Evan Sandhaus. 2008. The New York Times annotated corpus. http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp? catalogId=LDC2008T19.
34. Jayaram Sethuraman. 1994. A constructive definition of Dirichlet priors. Statistica Sinica, 4:639-650.
35. Robert Tibshirani. 1994. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58:267-288.
36. Stefan Wager, Sida Wang, and Percy Liang. 2013. Dropout training as adaptive regularization. In Proceedings of Advances in Neural Information Processing Systems, pages 351-359.
37. Hanna Wallach, David Mimno, and Andrew McCallum. 2009. Rethinking LDA: Why priors matter. In Proceedings of Advances in Neural Information Processing Systems.
38. Limin Yao, David Mimno, and Andrew McCallum. 2009. Efficient methods for topic model inference on streaming document collections. In Knowledge Discovery and Data Mining.
39. Ke Zhai, Jordan Boyd-Graber, Nima Asadi, and Mohamad Alkhouja. 2012. Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce. In Proceedings of World Wide Web Conference.