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Conceptual Modeling of Online Entertainment Programming Guide for Natural Language Interface

  • Harry Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6177)

Abstract

This paper describes a new novel approach to the conceptual modeling of text-based electronic programming guide (EPG) for broadcast TV programs by using a large text corpus constructed from the EPG metadata source. Two empirical experiments are carried out to evaluate the EPG-specific language models created using the new algorithm in context of natural language (NL) based information retrieval systems. The experimental results show the effectiveness of the algorithm for developing low-complexity concept models with high coverage for the user’s language models associated with both typed and spoken queries when interacting with a NL based EPG search interface.

Keywords

Natural language modeling linguistic properties of entertainment language electronic programming guide metadata harvesting 

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References

  1. 1.
    Writtenburg, K., Lanning, T., Schwenke, D., Shubin, H., Vetro, A.: The Prospects for Unrestricted Speech Input for TV Content Search. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 352–359 (2006)Google Scholar
  2. 2.
    Johnston, M., D’Haro, L.-F., Levine, M., Renger, R.: A Multimodal Interface for Access to Content in the Home. In: ACL 2007, pp. 376–383 (2007)Google Scholar
  3. 3.
    Amores, J.G., Perez, G., Manchon, P.: A Multimodal and Multilingual Dialogue System for the Home Domain. In: ACL 2007, pp. 1–4 (2007)Google Scholar
  4. 4.
    Roark, B., Charniak, E.: Noun-phrase Co-Occurrence Statistics for Semi-Automatic Semantic Lexicon Construction. In: ACL 2007, pp. 1110–1116 (1998)Google Scholar
  5. 5.
    Widdows, D., Dorow, B.: A Graph Model for Unsupervised Lexical Acquisition. In: ACL 2002, pp. 1093–1099 (2002)Google Scholar
  6. 6.
    De Boni, M.: Automated Discovery of Telic Relationships for WordNet. In: Proc. of First International Word Net Conference (2002)Google Scholar
  7. 7.
    Widdows, D.: Unsupervised Methods for Developing Taxonomies by Combining Syntactic and Statistical Information. In: Proc. of HLT/NAACL 2003, pp. 197–204 (2003)Google Scholar
  8. 8.
    Rousu, J., Shawe-Taylor, J.: Efficient Computation of Gapped Substring Kernels on Large Alphabets. J. of Machine Learning Research 6, 1323–1344 (2005)MathSciNetGoogle Scholar
  9. 9.
    Kate, R.J., Mooney, R.J.: Using String-Kernels for Learning Semantic Parsers. In: Proc. of COLING/ACL 2006, pp. 913–920 (2006)Google Scholar
  10. 10.
    Beck, A., Borst, S., Ensor, B., Esteban, J.O., Hilt, V., Rimac, I., Walid, A.: New Challenges in Content Dissemination Networks. Bell Labs Technical Journal 13(3), 5–12 (2008)CrossRefGoogle Scholar
  11. 11.
    Mandelbrot, B.B.: An Information Theory of the Statistical Structure of Language. In: Communication Theory, pp. 503–512. Academic Press, New York (1953)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Harry Chang
    • 1
  1. 1.AT&T Labs – ResearchAustinUSA

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