Incorporating lexical priors into topic models

EACL 2012 - 13th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings

Volumn , Issue , 2012, Pages 204-213

  Jagarlamudi, Jagadeesh ^a   Iii, Hal Dauḿe ^a   Udupa, Raghavendra ^b  

^a UNIVERSITY OF MARYLAND   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS;

DOCUMENT CLUSTERING; MODEL USE; SEED WORDS; TOPIC DISTRIBUTIONS; TOPIC MODEL;

LINGUISTICS;

EID: 85032592822     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Andrzejewski, D. and Zhu, X. (2009). Latent dirichlet allocation with topic-in-set knowledge. In Proceedings of the NAACL HLT 2009 Workshop on Semi- Supervised Learning for Natural Language Processing, SemiSupLearn '09, pages 43-48, Morristown, NJ, USA. Association for Computational Linguistics.
2. Andrzejewski, D., Zhu, X., and Craven, M. (2009). Incorporating domain knowledge into topic modeling via dirichlet forest priors. In ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, pages 25-32, New York, NY, USA. ACM.
3. Basu, S., Ian, D., and Wagstaff, K. (2008). Constrained Clustering : Advances in Algorithms, Theory, and Applications. Chapman & Hall/CRC Pres.
4. Blei, D. and McAuliffe, J. (2008). Supervised topic models. In Advances in Neural Information Processing Systems 20, pages 121-128, Cambridge, MA. MIT Press.
5. Blei., D. M. and Lafferty., J. (2009). Topic models. In Text Mining: Theory and Applications. Taylor and Francis.
6. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. Journal ofMaching Learning Research, 3:993-1022.
7. Boyd-Graber, J., Blei, D. M., and Zhu, X. (2007). A topic model for word sense disambiguation. In Empirical Methods in Natural Language Processing.
8. Chang, J., Boyd-Graber, J., Wang, C., Gerrish, S., and Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In Neural Information Processing Systems.
9. Griffiths, T., Steyvers, M., and Tenenbaum, J. (2007). Topics in semantic representation. Psychological Review, 114(2):211-244.
10. Griffiths, T. L. and Steyvers, M. (2004). Finding scientific topics. Proceedings of National Academy of Sciences USA, 101 Suppl1:5228-5235.
11. Griffiths, T. L., Steyvers, M., Blei, D. M., and Tenenbaum, J. B. (2005). Integrating topics and syntax. In Advances in Neural Information Processing Systems, volume 17, pages 537-544.
12. Haghighi, A. and Klein, D. (2006). Prototype-driven learning for sequence models. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, HLT-NAACL '06, pages 320-327, Stroudsburg, PA, USA. Association for Computational Linguistics.
13. Hu, Y., Boyd-Graber, J., and Satinoff, B. (2011). Interactive topicmodeling. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, HLT '11, pages 248-257, Stroudsburg, PA, USA. Association for Computational Linguistics.
14. Johnson, M. and Goldwater, S. (2009). Improving nonparameteric Bayesian inference: Experiments on unsupervised word segmentation with adaptor grammars. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL '09, pages 317-325, Stroudsburg, PA, USA. Association for Computational Linguistics.
15. Lacoste-Julien, S., Sha, F., and Jordan, M. (2008). Disc LDA: Discriminative learning for dimensionality reduction and classification. In Proceedings of NIPS '08.
16. Lewis, D. D., Yang, Y., Rose, T. G., and Li, F. (2004). Rcv1: A new benchmark collection for text categorization research. J. Mach. Learn. Res., 5:361-397.
17. Meiľa, M. (2007). Comparing clusterings-An information based distance. J. Multivar. Anal., 98:873- 895.
18. Mitchell, T. M. (1997). Machine Learning. McGraw- Hill, New York.
19. Paul, M. and Girju, R. (2010). A two-dimensional topic-Aspect model for discovering multi-faceted topics. In AAAI.
20. Ramage, D., Hall, D., Nallapati, R., and Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1, EMNLP '09, pages 248- 256, Morristown, NJ, USA. Association for Computational Linguistics.
21. Thelen, M. and Riloff, E. (2002). A bootstrapping method for learning semantic lexicons using extraction pattern contexts. In In Proc. 2002 Conf. Empirical Methods in NLP (EMNLP).
22. Wagstaff, K., Cardie, C., Rogers, S., and Schrödl, S. (2001). Constrained k-means clustering with background knowledge. In Proceedings of the Eighteenth International Conference on Machine Learning, ICML '01, pages 577-584, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
23. Wallach, H. M. (2005). Topic modeling: beyond bagof- words. In NIPS 2005 Workshop on Bayesian Methods for Natural Language Processing.
24. Williamson, S., Wang, C., Heller, K. A., and Blei, D. M. (2010). The IBP compound dirichlet process and its application to focused topic modeling. In ICML, pages 1151-1158.