Meaning helps learning syntax

  • Isabelle Tellier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1433)


In this paper, we propose a new framework for the computational learning of formal grammars with positive data. In this model, both syntactic and semantic information are taken into account, which seems cognitively relevant for the modeling of natural language learning. The syntactic formalism used is the one of Lambek categorial grammars and meaning is represented with logical formulas. The principle of compositionality is admitted and defined as an isomorphism applying to trees and allowing to automatically translate sentences into their semantic representation(s). Simple simulations of a learning algorithm are extensively developed and discussed.


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  1. 1.
    P. W. Adriaans, Language Learning from a Categorial Perspective, Ph.D. thesis, University of Amsterdam, 1992.Google Scholar
  2. 2.
    J. R. Anderson, “Induction of Augmented Transition Networks”, Cognitive Science 1, p125–157, 1977.CrossRefGoogle Scholar
  3. 3.
    W. Buszkowski, G. Penn, “Categorial grammars determined from linguistic data by unification”, Studia Logica 49, p431–454, 1990.MATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    N. Chomsky, Aspects of the Theory of Syntax, Cambridge, MIT Press.Google Scholar
  5. 5.
    N. Chomsky, Language and Mind, Brace & World, 1968.Google Scholar
  6. 6.
    F. Denis, R. Gilleron, “PAC learning under helpful distributions”, Proceedings of the 8th ACM Workshop on Computational Learning Theory, p132–145, 1997.Google Scholar
  7. 7.
    D. R. Dowty, R. E. Wall, S. Peters, Introduction to Montague Semantics, Reidel, Dordrecht, 1989.Google Scholar
  8. 8.
    A. Finkel, I. Tellier: “A polynomial algorithm for the membership problem with categorial grammars”, Theoretical Computer Science 164, p207–221, 1996.MATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    E. M. Gold, “Language Identification in the Limit”, Information and Control 10, P447–474, 1967.MATHCrossRefGoogle Scholar
  10. 10.
    H. Hamburger, K. Wexler, “A mathematical Theory of Learning Transformational Grammar”, Journal of Mathematical Psychology 12, p137–177, 1975.MATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    J. C. Hill, “A computational model of language acquisition in the two-year-old”, Cognition and Brain Theory 6(3), p287–317, 1983.Google Scholar
  12. 12.
    M. Kanazawa, “Identification in the Limit of Categorial Grammars”, Journal of Logic, Language & Information, vol 5, nℴ2, p115–155, 1996.MATHMathSciNetGoogle Scholar
  13. 13.
    J. Lambek, “The mathematics of Sentence Structure”, American Mathematical Monthly, nℴ65, p154–170, 1958.MathSciNetCrossRefGoogle Scholar
  14. 14.
    P. Langley, “Language acquisition through error discovery”, Cognition and Brain Theory 5, p211–255, 1982.Google Scholar
  15. 15.
    M. Li, P. Vitanyi, “A theory of learning simple concepts under simple distributions”, SIAM J. Computing, 20(5), p915–935, 1991.MathSciNetCrossRefGoogle Scholar
  16. 16.
    R. Montague, Formal Philosophy; Selected papers of Richard Montague, Yale University Press, New Haven, 1974.Google Scholar
  17. 17.
    M. Moortgat, Categorial investigations, logical and linguistic aspects of the Lambek Calculus, Foris, Dordrecht, 1988.Google Scholar
  18. 18.
    R. T. Oehrle, E. Bach, D. Wheeler (eds.), Categorial Grammars and Natural Language Structure, Reidel, Dordrecht, 1988.Google Scholar
  19. 19.
    M. Pentus, “Lambek grammars are context-free”, in: 8th Annual IEEE Symposium on Logic in Computer Science, Montreal, Canada, p429–433, 1992.Google Scholar
  20. 20.
    S. Pinker, “Formal models of language learning”, Cognition 7, p217–283, 1979.CrossRefGoogle Scholar
  21. 21.
    Y. Sakakibara, “Efficient learning of context-free grammars from positive structural examples”, Information & Computation 97, p23–60, 1992.MATHMathSciNetCrossRefGoogle Scholar
  22. 22.
    S. Schieber, “Evidence against the context-freeness of natural languages”, Linguistics and Philosophy 8, p333–343, 1995.CrossRefGoogle Scholar
  23. 23.
    T. Shinohara, “Inductive inference of monotonic formal systems from positive data”, p339–351 in: Algorithmic Learning Theory, S. Arikara, S. Goto, S. Ohsuga & T. Yokomori (eds), Tokyo: Ohmsha and New York and Berlin: Springer.Google Scholar
  24. 24.
    L. G. Valiant, “A theory of the learnable”, Communication of the ACM, p1134–1142, 1984.Google Scholar
  25. 25.
    K. Wexler, P. Culicover, Formal Principles of Language Acquisition, Cambridge, MIT Press.Google Scholar
  26. 26.
    W. Zielonka, “Axiomatizability of Ajdukiewicz-Lambek calculus by means of cancellations schemes”, Zeischrift für Mathematsche Logik und grunlagen der Mathematik 27, p215–224, 1981.MATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Isabelle Tellier
    • 1
  1. 1.LIFL and Université Charles de Gaulle-lille3 (UFR IDIST)Villeneuve d'Ascq CedexFrance

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