Sometimes average is best: The importance of averaging for prediction using MCMC inference in topic modeling

EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Volumn , Issue , 2014, Pages 1752-1757

  Nguyen, Viet An ^a   Boyd Graber, Jordan ^b   Resnik, Philip ^a  

^a UNIVERSITY OF MARYLAND   (United States)

^b UCB 450   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MARKOV PROCESSES;

CUT CORNERS; LATENT VARIABLE MODELS; MARKOV CHAIN MONTE CARLO; POSTERIOR DISTRIBUTIONS; SINGLE SAMPLE; TOPIC MODELING;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84926039999     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/d14-1182     Document Type: Conference Paper

References (29)

1. Christophe Andrieu, Nando de Freitas, Arnaud Doucet, and Michael I. Jordan. 2003. An introduction to MCMC for machine learning. Machine Learning, 50(1-2):5-43.
2. Arthur Asuncion, Max Welling, Padhraic Smyth, and Yee Whye Teh. 2009. On smoothing and inference for topic models. In UAI.
3. David M. Blei and Jon D. McAuliffe. 2007. Supervised topic models. In NIPS.
4. David M. Blei, Andrew Ng, and Michael Jordan. 2003. Latent Dirichlet allocation. JMLR, 3.
5. David M. Blei. 2012. Probabilistic topic models. Commun. ACM, 55(4):77-84, April.
6. David M. Blei. 2014. Build, compute, critique, repeat: Data analysis with latent variable models. Annual Review of Statistics and Its Application, 1(1):203-232.
7. Jordan Boyd-Graber and Philip Resnik. 2010. Holistic sentiment analysis across languages: Multilingual supervised latent Dirichlet allocation. In EMNLP.
8. Jonathan Chang. 2012. lda: Collapsed Gibbs sampling methods for topic models. http://cran.r-project.org/web/packages/lda/index.html. [Online; accessed 02-June-2014].
9. Qixia Jiang, Jun Zhu, Maosong Sun, and Eric P. Xing. 2012. Monte Carlo methods for maximum margin supervised topic models. In NIPS.
10. Yohan Jo and Alice H. Oh. 2011. Aspect and sentiment unification model for online review analysis. In WSDM.
11. Thorsten Joachims. 1999. Making large-scale SVM learning practical. In Advances in Kernel Methods - Support Vector Learning, chapter 11. Cambridge, MA.
12. Simon Lacoste-Julien, Fei Sha, and Michael I. Jordan. 2008. DiscLDA: Discriminative learning for dimensionality reduction and classification. In NIPS.
13. D. Liu and J. Nocedal. 1989. On the limited memory BFGS method for large scale optimization. Math. Prog.
14. Radford M. Neal. 1993. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR- 93-1, University of Toronto.
15. Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. 2012. SITS: A hierarchical nonparametric model using speaker identity for topic segmentation in multiparty conversations. In ACL.
16. Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. 2013. Lexical and hierarchical topic regression. In Neural Information Processing Systems.
17. Bo Pang and Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In ACL.
18. Daniel Ramage, David Hall, Ramesh Nallapati, and Christopher Manning. 2009. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In EMNLP.
19. Daniel Ramage, Christopher D. Manning, and Susan Dumais. 2011. Partially labeled topic models for interpretable text mining. In KDD, pages 457-465.
20. Philip Resnik and Eric Hardisty. 2010. Gibbs sampling for the uninitiated. Technical Report UMIACS-TR-2010-04, University of Maryland. http://drum.lib.umd.edu//handle/1903/10058.
21. Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, and Padhraic Smyth. 2004. The author-topic model for authors and documents. In UAI.
22. Sameer Singh, Michael Wick, and Andrew McCallum. 2012. Monte Carlo MCMC: Efficient inference by approximate sampling. In EMNLP, pages 1104-1113.
23. Mark Steyvers and Tom Griffiths. 2006. Probabilistic topic models. In T. Landauer, D. Mcnamara, S. Dennis, and W. Kintsch, editors, Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum.
24. Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, and David Mimno. 2009. Evaluation methods for topic models. In Leon Bottou and Michael Littman, editors, ICML.
25. Hanna M Wallach. 2008. Structured Topic Models for Language. Ph.D. thesis, University of Cambridge.
26. Chong Wang, David Blei, and Li Fei-Fei. 2009. Simultaneous image classification and annotation. In CVPR.
27. Hongning Wang, Yue Lu, and Chengxiang Zhai. 2010. Latent aspect rating analysis on review text data: A rating regression approach. In SIGKDD, pages 783-792.
28. Jun Zhu, Amr Ahmed, and Eric P. Xing. 2009. MedLDA: maximum margin supervised topic models for regression and classification. In ICML.
29. Jun Zhu, Ning Chen, Hugh Perkins, and Bo Zhang. 2014. Gibbs max-margin topic models with data augmentation. Journal of Machine Learning Research, 15:1073-1110.