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Recovering Implicit Information

  • Martha S. Palmer
  • Deborah A. Dahl
  • Rebecca J. Schiffman
  • Lynette Hirschman
  • Marcia Linebarger
  • John Dowding
Chapter
Part of the Linguistica Computazionale book series (LICO, volume 9)

Abstract

This paper describes the SDC PUNDIT (Prolog UNDerstands Integrated Text), system for processing natural language messages. PUNDIT, written in Prolog, is a highly modular system consisting of distinct syntactic, semantic and pragmatics components. Each component draws on one or more sets of data, including a lexicon, a broad-coverage grammar of English, semantic verb decompositions, rules mapping between syntactic and semantic constituents, and a domain model.

This paper discusses the communication between the syntactic, semantic and pragmatic modules that is necessary for making implicit linguistic information explicit. The key is letting syntax and semantics recognize missing linguistic entities as implicit entities, so that they can be labeled as such, and reference resolution can be directed to find specific referents for the entities. In this way the task of making implicit linguistic information explicit becomes a subset of the tasks performed by reference resolution. The success of this approach is dependent on marking missing syntactic constituents as elided and missing semantic roles as ESSENTIAL so that reference resolution can know when to look for referents.

Keywords

Noun Phrase Disk Drive Mapping Rule Semantic Role Spindle Motor 
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.

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References

  1. [1]
    Dahl, D.A., Focusing and Reference Resolution in PUNDIT, Presented at AAAI, Philadelphia, 1986.Google Scholar
  2. [2]
    Dowding, J. and L. Hirschman, Dynamic Translation for Rule Pruning in Restriction Grammar, submitted to AAAI-86, Philadelphia, 1986.Google Scholar
  3. [3]
    Fillmore, C.J., “The Case for Case”, in E. Bach and R.T. Harms (eds.), Universals in Linguistic Theory, Holt, Rinehart, and Winston, New York, 1968.Google Scholar
  4. [4]
    Gundel, J.K., “Zero-NP Anaphora in Russian”, Chicago Linguistic Society Parasession on Pronouns and Anaphora, 1980.Google Scholar
  5. [5]
    L. Hirschman and K. Puder, “Restriction Grammar in Prolog”, in M. Van Caneghem (ed.), Proc. of the First International Logic Programming Conference, Association pour la Diffusion et le Developpement de Prolog, Marseilles, 1982, 85–90.Google Scholar
  6. [6]
    Hirschman, L. and K. Puder, “Restriction Grammar: A Prolog Implementation”, in D.H.D. Warren and M. VanCaneghem (eds.), Logic Programming and its Applications, 1985.Google Scholar
  7. [7]
    Hirschman, L., “Conjunction in Meta-Restriction Grammar”, Journal of Logic Programming, 1986.Google Scholar
  8. [8]
    Kameyama, M., “Zero Anaphora: The Case of Japanese”, Ph.D. thesis, Stanford University, 1985.Google Scholar
  9. [9]
    Palmer, M. “Inference Driven Semantic Analysis”, Proceedings of the National Conference on Artificial Intelligence (AAA1–83), Washington, D.C., 1983.Google Scholar
  10. [10]
    Palmer, M.S., A Case for Rule Driven Semantic Processing. Proceedings of the 19th ACL Conference, June, 1981Google Scholar
  11. [11]
    Palmer, M.S., “Driving Semantics for a Limited Domain”, Ph.D. thesis, University of Edinburgh, 1985.Google Scholar
  12. [12]
    Palmer, M.S., et al., “The KERNEL text understanding system,” Artificial Intelligence, 63, 1993, 17–68.CrossRefGoogle Scholar
  13. [13]
    Pereira, F.C.N. and D.H.D. Warren,“ Definite Clause Grammars for Language Analysis — A Survey of the Formalism and a Comparison with Augmented Transition Networks.” Artificial Intelligence, 13, 1980, 231–278.CrossRefGoogle Scholar
  14. [14]
    Sager, N., Natural Language Information Processing: A Computer Grammar of English and Its Applications, Addison-Wesley, Reading, MA. 1981.Google Scholar
  15. [15]
    Sidner, C.L., “Towards a Computational Theory of Definite Anaphora Comprehension in English Discourse”, MIT-AI TR-537, Cambridge, MA, 1979.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1994

Authors and Affiliations

  • Martha S. Palmer
    • 1
  • Deborah A. Dahl
    • 1
  • Rebecca J. Schiffman
    • 1
  • Lynette Hirschman
    • 1
  • Marcia Linebarger
    • 1
  • John Dowding
    • 1
  1. 1.R&DSDC — A Burroughs CompanyUSA

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