Abstract
Expanding a topic into its constituent terms used by a population is an important problem in social space analytics. A topic is defined as a concept representing the semantic “aboutness” of a set of other concepts. What a topic is really “about” may differ across different populations, and discovering this provides sound insights about the population itself. In this paper, we propose an approach for topic expansion, inspired by models from cognitive science – specifically, episodic and semantic memory. We propose an episodic hypothesis that asserts a relationship between a topic and other concepts that are collectively about the topic. The canonical form of this algorithm, while shown to produce good results, does not run in interactive response time. We then propose several simplifications over the canonical form to provide interactive response times without reducing too much on the quality of the results. The proposed simplifications also help in creating topical clusters on repositories where there is not enough representation of different terms for the topic.
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
Kulkarni, S., Srinivasa, S., Arora, R.: Topic expansion using a term co-occurrence graph. Technical report (2012), http://rootset.iiitb.ac.in/data/138_topic_expansion_using_tcg.pdf
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Boyd-Graber, J., Blei, D., Zhu, X.: A topic model for word sense disambiguation. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1024–1033 (2007)
Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)
Dorow, B., Widdows, D., Ling, K., Eckmann, J.P., Sergi, D., Moses, E.: Using curvature and markov clustering in graphs for lexical acquisition and word sense discrimination. arXiv:cond-mat/0403693 (2004)
Lee, Y.K., Ng, H.T.: An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing - EMNLP 2002, vol. 10, pp. 41–48. ACL, Stroudsburg (2002)
Rachakonda, A.R.: A cognitive model for mining latent semantics in unstructured text. In: Proceedings of VLDB PhD Workshop, Istanbul, Turkey (2012)
Rachakonda, A.R., Srinivasa, S., Kulkarni, S., Srinivasan, M.S.: Mining analytic semantics from unstructured text. Technical report (2012), http://rootset.iiitb.ac.in/data/139_mas.pdf
Zellig Harris, S.: Distributional structure. Word 10, 146–162 (1954)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kulkarni, S., Srinivasa, S., Arora, R. (2013). Cognitive Modeling for Topic Expansion. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2013 Conferences. OTM 2013. Lecture Notes in Computer Science, vol 8185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41030-7_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-41030-7_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41029-1
Online ISBN: 978-3-642-41030-7
eBook Packages: Computer ScienceComputer Science (R0)