Automatic Inference of DATR Theories

  • Petra Barg
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

An approach for the automatic acquisition of linguistic knowledge from unstructured data is presented. The acquired knowledge is represented in the lexical knowledge representation language DATR. A set of transformation rules that establish inheritance relationships and a default-inference algorithm make up the basis components of the system. Since the overall approach is not restricted to a special domain, the heuristic inference strategy uses criteria to evaluate the quality of a DATR theory, where different domains may require different criteria. The system is applied to the linguistic learning task of German noun inflection.

Keywords

Nism Suffix 

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References

  1. Barg, P. (1995): Automatischer Erwerb von linguistischem Wissen: ein An-satz zur Inferenz von DATR-Theorien. Dissertation, Heinrich-Heine-Universität Düsseldorf.Google Scholar
  2. Brachman, R.J., and Schmölze, J.G. (1985): An Overview of the KL-ONE Knowledge Representation System. Cognitive Science, 9, 171–216. Evans, R., and Gazdar, G. (1989): Inference in DATR. Proc. of the 4th Conference of the European Chapter of the Association for Computational Linguistics, 66–71.Google Scholar
  3. Evans, R., and Gazdar, G. (eds.) (1990): The DATR Papers: February 1990 (= Cognitive Science Research Paper 139). School of Cognitive and Computing Sciences, Univerity of Sussex, Brighton, England.Google Scholar
  4. Lebowitz, M. (1987): Experiments with Incremental Concept Formation: UNI- MEM. Machine Learning, 2, 103–138.Google Scholar
  5. Light, M. (1994): Classification in Feature-based Default Inheritance Hierar-chies. In: H. Trost (ed.): KONVENS ’94: Verarbeitung natürlicher Sprache. Österreichische Gesellschaft für Artificial Intelligence, Wien, 220–229.Google Scholar
  6. Michalski, R. (1983): A Theory and Methodology of Inductive Learning. Ar-tificial Intelligence, 20(2), 111161.CrossRefGoogle Scholar
  7. Michalski, R. (1986): Understanding the nature of learning: Issues and re-search directions. In: R.S. Michalski, J.G. Carbonell and T.M. Mitchell (eds.): Machine Learning: An Artificial Intelligence Approach. Los Altos, Morgan Kauf-mann, vol. 2, 325.Google Scholar
  8. Mitchell, T.M. (1982): Generalization as search. Artificial Intelligence, 18, 203–226.CrossRefGoogle Scholar
  9. Powers, D., and Reeker, L. (1991): Machine Learning of Natural Language and Ontology (Proc. A A AI Spring Symposium ). Kaiserslautern.Google Scholar
  10. Stepp, R.E., and Michalski, R.S. (1986): Conceptual Clustering: Inventing Goal-Oriented Classifications of Structured Objects. In: R.S. Michalski, J.G. Carbonell and T.M. Mitchell (eds.): Machine Learning: An Artificial Intelligence Approach. Los Altos, Morgan Kaufmann, vol. 2, 471498.Google Scholar
  11. Wurzel, W. (1970): Studien zur deutschen Lautstruktur. Akademie-Verlag, Berlin.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

  • Petra Barg
    • 1
  1. 1.Seminar für Allgemeine SprachwissenschaftUniversität DüsseldorfDüsseldorfGermany

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