Skip to main content
SpringerLink
Log in

Hybrid Semantic Analysis System – ATIS Data Evaluation

  • Conference paper
Advanced Data Mining and Applications (ADMA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6441))

Included in the following conference series:

  • 3007 Accesses

Abstract

In this article we show a novel method of semantic parsing. The method deals with two main issues. First, it is developed to be reliable and easy to use. It uses a simple tree-based semantic annotation and it learns from data. Second, it is designed to be used in practical applications by incorporating a method for data formalization into the system. The system uses a novel parser that extends a general probabilistic context-free parser by using context for better probability estimation. The semantic parser was originally developed for Czech data and for written questions. In this article we show an evaluation of the method on a very different domain – ATIS corpus. The achieved results are very encouraging considering the difficulties connected with the ATIS corpus.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pieraccini, R., Tzoukermann, E., Gorelov, Z., Levin, E., Lee, C.-H., Gauvain, J.-L.: Progress Report on the Chronus System: ATIS Benchmark Results. In: Proc. of the workshop on Speech and Natural Language, pp. 67–71. Association for Computational Linguistics, New York (1992)

    Chapter  Google Scholar 

  2. He, Y., Young, S.: Semantic processing using the Hidden Vector State model. Computer Speech and Language 19(1), 85–106 (2005)

    Article  Google Scholar 

  3. Iosif, E., Potamianos, A.: A soft-clustering algorithm for automatic induction of semantic classes. In: Interspeech 2007, Antwerp, Belgium, pp. 1609–1612 (2007)

    Google Scholar 

  4. Jeong, M., Lee, G.: Practical use of non-local features for statistical spoken language understanding. Computer Speech and Language 22(2), 148–170 (2008)

    Article  MathSciNet  Google Scholar 

  5. Raymond, C., Riccardi, G.: Generative and discriminative algorithms for spoken language understanding. In: Interspeech 2007, Antwerp, Belgium, pp. 1605–1608 (2007)

    Google Scholar 

  6. Dahl, D.A., et al.: ATIS3 Test Data, Linguistic Data Consortium, Philadelphia (1995)

    Google Scholar 

  7. Allen, J.: Natural Language Understanding. Benjamin/Cummings Publ. Comp. Inc., Redwood City, California (1995)

    MATH  Google Scholar 

  8. Grepl, M., Karlík, P.: Skladba ?e?tiny, Olomouc, Czech Republic (1998)

    Google Scholar 

  9. Habernal, I., Konopík, M.: Active Tags for Semantic Analysis. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2008. LNCS (LNAI), vol. 5246, pp. 69–76. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Jurafsky, D., Martin, J.: Speech and Language Processing. Prentice-Hall, Englewood Cliffs (2000)

    Google Scholar 

  11. Konopík, M.: Hybrid Semantic Analysis, PhD thesis, University of West Bohemia (2009)

    Google Scholar 

  12. Knuth, D.: The Art of Computer Programming, 2nd edn. Sorting and Searching, vol. 3. Addison-Wesley, Reading (1997)

    MATH  Google Scholar 

  13. Habernal, I., Konopík, M.: Semantic Annotation for the LingvoSemantics Project. In: Matoušek, V., Mautner, P. (eds.) TSD 2009. LNCS (LNAI), vol. 5729, pp. 299–306. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Konopík, M., Habernal, I.: Hybrid Semantic Analysis. In: Matoušek, V., Mautner, P. (eds.) TSD 2009. LNCS (LNAI), vol. 5729, pp. 307–314. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  15. He, Y., Zhou, D.: Discriminative training of the hidden vector state model for semantic parsing. IEEE Trans. on Knowl. and Data Eng. 21(1), 66–77 (2009)

    Article  MathSciNet  Google Scholar 

  16. Jurc̆íc̆ek, F.: Statistical approach to the semantic analysis of spoken dialogues. PhD thesis, University of West Bohemia, Faculty of Applied Sciences (2007)

    Google Scholar 

  17. Kate, J., Mooney, R.J.: Using string-kernels for learning semantic parsers. In: ACL- 44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Morristown, pp. 913–920 (2006)

    Google Scholar 

  18. Wang, Y., Deng, L., Acero, A.: An introduction to statistical spoken language understanding. IEEE Signal Processing Magazine 22(5), 16–31 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, University of West Bohemia, Univerzitní 22, CZ - 306 14, Plzeň, Czech Republic

    Ivan Habernal & Miloslav Konopík

Authors
  1. Ivan Habernal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miloslav Konopík
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, Sydney, NSW, Australia

    Longbing Cao

  2. College of Computer Science, Chongqing University, 400030, Chongqing, China

    Jiang Zhong  & Yong Feng  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Habernal, I., Konopík, M. (2010). Hybrid Semantic Analysis System – ATIS Data Evaluation. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17313-4_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17312-7

  • Online ISBN: 978-3-642-17313-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation