Abstract
End users can find topic model results difficult to interpret and evaluate. To address user needs, we present a semi-supervised hierarchical Dirichlet process for topic modeling that incorporates user-defined prior knowledge. Applied to a large electronic dataset, the generated topics are more fine-grained, more distinct, and align better with users’ assignments of topics to documents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
AlSumait, L., Barbará, D., Gentle, J., Domeniconi, C.: Topic Significance Ranking of LDA Generative Models. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part I. LNCS, vol. 5781, pp. 67–82. Springer, Heidelberg (2009)
Andrzejewski, D., Zhu, X.: Latent dirichlet allocation with topic-in-set knowledge. In: Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing, pp. 43–48 (2009)
Blei, D.M., McAuliffe, J.D.: Supervised topic models. In: Advances in Neural Information Processing Systems, NIPS (2007)
Blei, D.M., Ng, A., Jordan, M.: Latent dirichlet allocation. JMLR 3, 993–1022 (2003)
Escobar, M.D., West, M.: Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90, 577–588 (1995)
Jaccard, P.: Nouvelles recherches sur la distribution florale. Bulletin de la Société Vaudoise des Sciences Naturelles 44, 223–270 (1908)
Mimno, D., Wallach, H., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, pp. 262–272 (July 2011)
Perotte, A., Bartlett, N., Elhadad, N., Wood, F.: Hierarchically supervised latent dirichlet allocation. In: Advances in Neural Information Processing Systems, NIPS (2011)
Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: 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, pp. 248–256 (2009)
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical dirichlet processes. Journal of the American Statistical Association 101(476), 1566–1581 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xie, B., Passonneau, R.J. (2012). Supervised HDP Using Prior Knowledge. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds) Natural Language Processing and Information Systems. NLDB 2012. Lecture Notes in Computer Science, vol 7337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31178-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-31178-9_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31177-2
Online ISBN: 978-3-642-31178-9
eBook Packages: Computer ScienceComputer Science (R0)