Exploring the space of topic coherence measures

WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2015, Pages 399-408

  Röder, Michael ^a   Both, Andreas ^b   Hinneburg, Alexander ^c  

^a UNIVERSITY OF LEIPZIG   (Germany)

^b Unister GmbH   (Germany)

^c MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

Author keywords

Topic coherence; Topic evaluation; Topic model

Indexed keywords

INFORMATION RETRIEVAL; TEXT MINING;

BENCHMARK DATASETS; INTERPRETABILITY; STANDING PROBLEMS; SYSTEMATIC SEARCHES; TOPIC EVALUATION; TOPIC MODEL; TOPIC MODELING; TOPIC RELEVANCE;

SEARCH ENGINES;

EID: 84926673043     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2684822.2685324     Document Type: Conference Paper

References (18)

1. th Int. Conf. on Computational Semantics (IWCS'13), pages 13-22, 2013.
2. L. Alsumait, D. Barbará, J. Gentle, and C. Domeniconi. Topic significance ranking of lda generative models. In Proc. of the Europ. Conf. on Machine Learning and Knowledge Disc. in Databases: Part I, ECML PKDD '09, pages 67-82, 2009.
3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
4. G. Bouma. Normalized (pointwise) mutual information in collocation extraction. In From Form to Meaning: Processing Texts Automatically, Proc. of the Biennial GSCL Conference 2009, pages 31-40, Tübingen, 2009.
5. L. Bovens and S. Hartmann. Bayesian Epistemology. Oxford University Press, 2003.
6. J. Chang, S. Gerrish, C. Wang, J. L. Boyd-graber, and D. M. Blei. Reading tea leaves: How humans interpret topic models. In Advances in Neural Information Processing Systems 22, pages 288-296. 2009.
7. I. Douven and W. Meijs. Measuring coherence. Synthese, 156(3):405-425, 2007.
8. B. Fitelson. A probabilistic theory of coherence. Analysis, 63(279):194-199, 2003.
9. A. Hinneburg, R. Preiss, and R. Schröder. Topicexplorer: Exploring document collections with topic models. In ECML/PKDD (2), pages 838-841, 2012.
10. J. H. Lau, D. Newman, and T. Baldwin. Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality. In Proc. of the Europ. Chap. of the Assoc. for Comp. Ling., 2014.
11. D. Mimno and D. Blei. Bayesian checking for topic models. In Proc. of the Conf. on Empirical Methods in Natural Language Processing, pages 227-237. 2011.
12. D. Mimno, H. M. Wallach, E. Talley, M. Leenders, and A. McCallum. Optimizing semantic coherence in topic models. In Proc. of the Conf. on Empirical Methods in Natural Language Processing, pages 262-272. 2011.
13. D. Newman, J. H. Lau, K. Grieser, and T. Baldwin. Automatic evaluation of topic coherence. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 100-108. 2010.
14. E. Olsson. What is the problem of coherence and truth? The Jour. of Philosophy, 99(5):246-272, 2002.
15. M. Röder, M. Speicher, and R. Usbeck. Investigating quality raters' performance using interface evaluation methods. In Informatik 2013, pages 137-139. GI, 2013.
16. F. Rosner, A. Hinneburg, M. Röder, M. Nettling, and A. Both. Evaluating topic coherence measures. CoRR, abs/1403.6397, 2014.
17. T. Shogenji. Is coherence truth conducive? Analysis, 59(264):338-345, 1999.
18. K. Stevens, P. Kegelmeyer, D. Andrzejewski, and D. Buttler. Exploring topic coherence over many models and many topics. In Proc. of the 2012 Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL '12, pages 952-961, 2012.