Abstract
This paper proposes a novel topic inference framework that is built on the scalability and adaptability of mutual information (MI) techniques. The framework is designed to systematically construct a more robust language model (LM) for topic-oriented search terms in the domain of electronic programming guide (EPG) for broadcast TV programs. The topic inference system identifies the most relevant topics implied from a search term, based on a simplified MI-based classifier trained from a highly structured XML-based text corpus, which is derived from continuously updated EPG data feeds. The proposed framework is evaluated against a set of EPG-specific queries from a large user population collected from a real world web-based IR system. The MI-base topic inference system is able to achieve 98 percent accuracy in recall measurement and 82 percent accuracy in precision measurement on the test set.
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References
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. J. Am. Soc. Inform. Science 41(6), 391–407 (1990)
Bellegarda, J.: Latent Semantic Mapping. IEEE Signal Processing Magazine 22, 70–80 (2005)
Papadimitriou, C.H., Raghavan, P., Tamaki, H., Vempala, S.: Latent Semantic Indexing: A Probabilistic Analysis. In: Proc. 17th ACM Symp. Princeples Database Systems, pp. 159–168 (1998)
Hofmann, T.: Probabilistic latent Semantic Analysis. Uncertainty in Artificial Intelligence (1999)
Landauer, T.K., Dumais, S.: A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. Psychological Review 104, 211–214 (1997)
Recchia, G., Jones, M.N.: More Data Trumps Smarter Algorithms: Comparing Pointwise Mutual Information with Latent Semantic Analysis. Behavior Research Methods 41(3), 647–656 (2009)
Budiu, R., Royer, C., Pirolli, P.L.: Modeling Information Scent: A Comparison of LSA, PMI, and GLAS Similarity Measure on Common Tests and Corpora. In: Proc. of the 8th Annual Conference of the Recherche d’Information Assistee Par Ordinateur (2005)
Chang, H.M.: Conceptual Modeling of Online Entertainment Programming Guide for Natural Language Interface. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds.) NLDB 2010. LNCS, vol. 6177, pp. 188–195. Springer, Heidelberg (2010)
Berger, A., Lafferty, J.: Information Retrieval as Statistical Translation. In: Proc. of the 22nd ACM Conference on Research and Development in Information Retrieval, pp. 222–229 (1999)
Ramos, J.: Using TF-IDF to Determine Word Relevance in Document Queries. In: Proc. of the First Instructional Conference on Machine Learning (2003)
Magerman, D.M., Marcus, M.P.: Parsing a Natural Language Using Mutual Information Statistics. In: Proc. of the 8th National Conference on Artificial Intelligence, pp. 984–989 (1990)
Jang, M.-G., Myaeng, S.H., Park, S.Y.: Using Mutual Information to Resolve Query Translation Ambiguities and Query Term Weighting. In: Proc. of the 37th Annual Meeting of Association of Computational Linguistics, pp. 223–228 (1999)
Peters, J.: Semantic Text Clusters and Word Classes – The Dualism of Mutual Information and Maximum Likelihood. In: Proc. of the Workshop on Language Modeling and Information Retrieval, pp. 55–59 (2001)
Dhillon, I.S., Mallela, S., Modha, D.S.: Information-Theoretic Co-Clustering. In: Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 89–98 (2003)
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
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Chang, H. (2011). Topics Inference by Weighted Mutual Information Measures Computed from Structured Corpus. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_7
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DOI: https://doi.org/10.1007/978-3-642-22327-3_7
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