Skip to main content

Using a More Powerful Teacher to Reduce the Number of Queries of the L* Algorithm in Practical Applications

  • Conference paper
Progress in Artificial Intelligence (EPIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3808))

Included in the following conference series:

Abstract

In this work we propose to use a more powerful teacher to effectively apply query learning algorithms to identify regular languages in practical, real-world problems. More specifically, we define a more powerful set of replies to the membership queries posed by the L* algorithm that reduces the number of such queries by several orders of magnitude in a practical application. The basic idea is to avoid the needless repetition of membership queries in cases where the reply will be negative as long as a particular condition is met by the string in the membership query. We present an example of the application of this method to a real problem, that of inferring a grammar for the structure of technical articles.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Gold, E.M.: Complexity of automaton identification from given data. Information and Control 37, 302–320 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  2. Pitt, L., Warmuth, M.: The minimum consistent DFA problem cannot be approximated within any polynomial. Journal of ACM 40, 95–142 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  3. Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75, 86–106 (1987)

    Article  MathSciNet  Google Scholar 

  4. Gold, E.M.: System identification via state characterization. Automatica 8, 621–636 (1972)

    Article  MATH  MathSciNet  Google Scholar 

  5. Schapire, R.E.: The Design and Analysis of Efficient Learning Algorithms. MIT Press, Cambridge (1992)

    Google Scholar 

  6. Nevill-Manning, C., Witten, I.H., Maulsby, D.L.: Modeling sequences using grammars and automata. In: Proceedings Canadian Machine Learning Workshop, pp. 15–18 (1994)

    Google Scholar 

  7. Hsu, C.N., Dung, M.T.: Generating finite-state transducers for semi-structured data extraction from the web. Information Systems 23, 521–538 (1998)

    Article  Google Scholar 

  8. Witten, I.H.: Adaptive text mining: inferring structure from sequences. Journal of Discrete Algorithms 2, 137–159 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  9. Laender, A.H.F., Ribeiro-Neto, B.A., da Silva, A.S., Teixeira, J.S.: A brief survey of web data extraction tools. SIGMOD Record 31, 84–93 (2002)

    Article  Google Scholar 

  10. Ribeiro-Neto, B.A., Laender, A.H.F., da Silva, A.S.: Extracting semi-structured data through examples. In: Proceedings of the 1999 ACM CIKM International Conference on Information and Knowledge Management, pp. 94–101. ACM, New York (1999)

    Chapter  Google Scholar 

  11. Adelberg, B.: NoDoSE - a tool for semi-automatically extracting semi-structured data from text documents. In: Proceedings ACM SIGMOD International Conference on Management of Data, pp. 283–294 (1998)

    Google Scholar 

  12. Califf, M.E., Mooney, R.J.: Relational learning of pattern-match rules for information extraction. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence, pp. 328–334 (1999)

    Google Scholar 

  13. Soderland, S.: Learning information extraction rules for semi-structured and free text. Machine Learning 34, 233–272 (1999)

    Article  MATH  Google Scholar 

  14. Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)

    Google Scholar 

  15. Martins, A.L., Pinto, H.S., Oliveira, A.L.: Towards automatic learning of a structure ontology for technical articles. In: Semantic Web Workshop at SIGIR 2004 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Martins, A.L., Pinto, H.S., Oliveira, A.L. (2005). Using a More Powerful Teacher to Reduce the Number of Queries of the L* Algorithm in Practical Applications. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_33

Download citation

  • DOI: https://doi.org/10.1007/11595014_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30737-2

  • Online ISBN: 978-3-540-31646-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics