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Principle-Based Parsing for Machine Translation

  • Bonnie Jean Dorr
Chapter
Part of the Studies in Linguistics and Philosophy book series (SLAP, volume 44)

Abstract

This chapter describes a syntactic parsing model that accommodates cross-linguistic uniform machine translation without relying on language specific context-free rules. Parsing systems typically use grammars that describe language with complicated rules that spell out the details of their application. ATN-based systems (Woods, 1970; Bates, 1978) have several hundred grammar arcs, each with detailed tests and actions; augmented phrase-structure grammars, as used in Diagram (Robinson, 1982), spell out the type, position, and probability of occurrence of constituents in a given phrase; and the GPSG approach (Gazdar et al., 1985) uses a ‘slash-category’ mechanism to incorporate long-distance relations directly into the grammar rules.1 Such systems do not work in the context of translation across several languages: the rules of a given grammar are painstakingly tailored to describe a single language, thus forcing a loss of linguistic generalization and limiting the addition of new languages.2

Keywords

Machine Translation Target Language Structure Building Lexical Entry Argument Structure 
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. Abney, S.: 1986, ‘Functional Elements and Licensing’, unpublished paper presented at GLOW (Generative Linguists of the Old World) Conference, Gerona, Spain.Google Scholar
  2. Barton, E.: 1984, Toward a Principle-Based Parser, MIT Artificial Intelligence Laboratory Memo 788, Massachusetts Institute of Technology, Cambridge, Massachusetts.Google Scholar
  3. Bates, M.: 1978, ‘The Theory and Practice of Augmented Transition Network Grammars’, in L. Bolc (ed.), Natural Language Communication with Computers, (Lecture Notes in Computer Science, 63), Springer-Verlag, New York, pp. 191–254.Google Scholar
  4. Chomsky, N.: 1981, Lectures on Government and Binding: The Pisa Lectures, Foris, Dordrecht, Holland.Google Scholar
  5. Chomsky, N.: 1982, Some Concepts and Consequences of the Theory of Government and Binding, MIT Press, Cambridge, Massachusetts.Google Scholar
  6. Dorr, B.: 1987, UNITRAN: A Principle-Based Approach to Machine Translation, MIT Artificial Intelligence Laboratory Technical Report 1000, Massachusetts Institute of Technology, Cambridge, Massachusetts.Google Scholar
  7. Dorr, B.: 1988, A Lexical Conceptual Approach to Generation for Machine Translation, MIT Artificial Intelligence Laboratory Memo 1015, Massachusetts Institute of Technology, Cambridge, Massachusetts.Google Scholar
  8. Dorr, B.: 1990, Lexical Conceptual Structure and Machine Translation, Ph.D. dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts.Google Scholar
  9. Earley, J.: 1970, ‘An Efficient Context-Free Parsing Algorithm’, Communications of the Association for Computing Machinery 14, 453–460.Google Scholar
  10. Frazier, L.: 1986, ‘Natural Classes in Language Processing’, unpublished paper presented at the Cognitive Science Seminar, Cognitive Science Center, Massachusetts Institute of Technology, Cambridge, Massachusetts.Google Scholar
  11. Gazdar, G., E. Klein, G. Pullum, and I. Sag: 1985, Generalized Phrase Structure Grammar, Basil Blackwell, Oxford, England.Google Scholar
  12. Hale, K.: 1973, ‘A Note on Subject-Object Inversion in Navajo’, in B. Kachrue, R. Lees, J. Malkiel, A. Pietrangeli, and F. Saporta, Issues in Linguistics: Papers in Honor of Henry and Renee Kahane, University of Illinois Press, Urbana, Illinois, pp. 300–309.Google Scholar
  13. Jaeggli, A: 1981, Topics in Romance Syntax, Foris, Dordrecht, Holland.Google Scholar
  14. Robinson, J.: 1982, ‘DIAGRAM: A Grammar for Dialogues’, Communications of the Association for Computing Machinery 25, 27–47.CrossRefGoogle Scholar
  15. Sharp, R.: 1985, A Model of Grammar Based on Principles of Government and Binding, M.S. dissertation, Department of Computer Science, University of British Columbia, Vancouver, British Columbia.Google Scholar
  16. Slocum, J.: 1984, METAL: The LRC Machine Translation System, paper presented at the ISSCO Tutorial on Machine Translation, Lugano, Switzerland, Linguistics Research Center, University of Texas, Austin, Texas.Google Scholar
  17. Slocum, J.: 1985, ‘A Survey of Machine Translation: Its History, Current Status, and Future Prospects’, Computational Linguistics 11, 1–17.Google Scholar
  18. Slocum, J. and W. Bennett: 1985, ‘The LRC Machine Translation System’, Computational Linguistics 11, 111–121.Google Scholar
  19. Van Riemsdijk, H. and E. Williams: 1986, Introduction to the Theory of Grammar, MIT Press, Cambridge, Massachusetts.Google Scholar
  20. Woods, W.: 1970, ‘Transition Network Grammars for Natural Language Analysis’, Communications of the Association for Computing Machinery 13, 591–606.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1991

Authors and Affiliations

  • Bonnie Jean Dorr
    • 1
  1. 1.Department of Computer ScienceUniversity of Maryland at College ParkCollege ParkUSA

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