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Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing

  • Lappoon R. Tang
  • Raymond J. Mooney
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2167)

Abstract

In this paper, we explored a learning approach which combines different learning methods in inductive logic programming (ILP) to allow a learner to produce more expressive hypotheses than that of each individual learner. Such a learning approach may be useful when the performance of the task depends on solving a large amount of classification problems and each has its own characteristics which may or may not fit a particular learning method. The task of semantic parser acquisition in two different domains was attempted and preliminary results demonstrated that such an approach is promising.

Keywords

Inductive Logic Minimum Description Length Inductive Logic Programming Logical Query Language Bias 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    John M. Zelle and Raymond J. Mooney: Learning to Parse Database Queries Using Inductive Logic Programming. Proceedings of the Thirteenth National Conference on Artificial Intelligence (1996) 1050–1055Google Scholar
  2. 2.
    J. Ross Quinlan: Learning Logical Definitions from Relations. Machine Learning 5 (1990) 239–266Google Scholar
  3. 3.
    John M. Zelle and Raymond J. Mooney: Combining Top-Down and Bottom-Up Methods in Inductive Logic Programming. Proceedings of the Eleventh International Conference on Machine Learning (1994) 343–351Google Scholar
  4. 4.
    Boonserm Kijsirikul and Masayuki Numao and Masamichi Shimura: Discrimination-Based Constructive Induction of Logic Programs. Proceedings of the Tenth National Conference on Artificial Intelligence (1992) 44–49Google Scholar
  5. 5.
    Bojan Cestnik: Estimating probabilities: A crucial task in machine learning. Proceedings of the Ninth European Conference on Artificial Intelligence (1990) 147–149Google Scholar
  6. 6.
    Jorma Rissanen: Modeling by Shortest Data Description. Automatica 14 (1978) 465–471zbMATHCrossRefGoogle Scholar
  7. 7.
    Ashwin Srinivasan, Stephen Muggleton, Michael Bain: The Justification of Logical Theories based on Data Compression. Machine Intelligence 13 (1994) 87–121Google Scholar
  8. 8.
    Stephen Muggleton and W. Buntine: Machine Invention of First-order Predicates by Inverting Resolution. Proceedings of the Fifth International Conference on Machine Learning (1988) 339–352Google Scholar
  9. 9.
    G. G. Hendrix and E. Sacerdoti and D. Sagalowicz and J. Slocum: Developing a Natural Language Interface to Complex Data. ACM Transactions on Database Systems 3 (1978) 105–147CrossRefGoogle Scholar
  10. 10.
    Scott Miller and David Stallard and Robert Bobrow and Richard Schwartz: A Fully Statistical Approach to Natural Language Interfaces. Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics (1996) 55–61Google Scholar
  11. 11.
    Roland Kuhn and Renato De Mori: The Application of Semantic Classification Trees to Natural Language Understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995) 449–460CrossRefGoogle Scholar
  12. 12.
    Cynthia A. Thompson and Raymond J. Mooney: Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces. Proceedings of the Sixteenth National Conference on Artificial Intelligence (1999) 487–493Google Scholar
  13. 13.
    Mary Elaine Califf and Raymond J. Mooney: Relational Learning of Pattern-Match Rules for Information Extraction. Proceedings of the Sixteenth National Conference on Artificial Intelligence (1999) 328–334Google Scholar
  14. 14.
    S. Muggleton, A. Srinivasan, and M. Bain: Compression, significance and accuracy. Proceedings of the Ninth International Machine Learning Conference (1992) 338–347Google Scholar
  15. 15.
    Attilio Giordana, Filippo Neri, Lorenza Saitta, and Marco Botta: Integrating Multiple Learning Strategies in First Order Logics. Machine Learning 27(3): 209–240 (1997)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Lappoon R. Tang
    • 1
  • Raymond J. Mooney
    • 1
  1. 1.Department of Computer SciencesUniversity of TexasAustin

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