Distributional Learning of Abstract Categorial Grammars

  • Ryo Yoshinaka
  • Makoto Kanazawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6736)


Recent studies on grammatical inference have demonstrated the benefits of the learning strategy called “distributional learning” for context-free and multiple context-free languages. This paper gives a comprehensive view of distributional learning of “context-free” formalisms (roughly in the sense of Courcelle 1987) in terms of abstract categorial grammars, in which existing “context-free” formalisms can be encoded.


Polynomial Time Atomic Type Lexical Entry Principal Typing Membership Query 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Clark, A., Eyraud, R.: Polynomial identification in the limit of substitutable context-free languages. Journal of Machine Learning Research 8, 1725–1745 (2007)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Yoshinaka, R.: Learning mildly context-sensitive languages with multidimensional substitutability from positive data. In: Gavaldà, R., Lugosi, G., Zeugmann, T., Zilles, S. (eds.) ALT 2009. LNCS, vol. 5809, pp. 278–292. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Yoshinaka, R.: Polynomial-time identification of multiple context-free languages from positive data and membership queries [19], pp. 230–244Google Scholar
  4. 4.
    Yoshinaka, R., Clark, A.: Polynomial time learning of some multiple context-free languages with a minimally adequate teacher. In: Proceedings of the 15th Conference on Formal Grammar, Copenhagen, Denmark (2010)Google Scholar
  5. 5.
    Clark, A.: Towards general algorithms for grammatical inference. In: Hutter, M., Stephan, F., Vovk, V., Zeugmann, T. (eds.) ALT 2010. LNCS, vol. 6331, pp. 11–30. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    de Groote, P.: Towards abstract categorial grammars. In: Association for Computational Linguistics, Proceedings of the Conference on 39th Annual Meeting and 10th Conference of the European Chapter, pp. 148–155 (2001)Google Scholar
  7. 7.
    de Groote, P.: Tree-adjoining grammars as abstract categorial grammars. In: TAG+6, Proceedings of the 6th International Workshop on Tree Adjoining Grammars and Related Frameworks, Università di Venezia, pp. 145–150 (2002)Google Scholar
  8. 8.
    de Groote, P., Pogodalla, S.: On the expressive power of abstract categorial grammars: Representing context-free formalisms. Journal of Logic, Language and Information 13(4), 421–438 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Courcelle, B.: An axiomatic definition of context-free rewriting and its application to NLC graph grammars. Theoretical Computer Science 55(2-3), 141–181 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hindley, J.R.: Basic Simple Type Theory. Cambridge University Press, Cambridge (1997)CrossRefzbMATHGoogle Scholar
  11. 11.
    Hirokawa, S.: Balanced formulas, BCK-minimal formulas and their proofs. In: Nerode, A., Taitslin, M.A. (eds.) LFCS 1992. LNCS, vol. 620, pp. 198–208. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  12. 12.
    Babaev, A., Soloviev, S.: A coherence theorem for canonical morphism in cartesian closed categories. Zapiski nauchnykh Seminarov Lenigradskogo Otdeleniya matematichskogo lnstituta im. V.A. Steklova An SSSR 88, 3–29 (1979)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Mints, G.: A short introduction to intuitionistic logic. Kluwer Academic/Plenum Publishers, New York (2000)zbMATHGoogle Scholar
  14. 14.
    Bunder, M.W.: Proof finding algorithms for implicational logics. Theoretical Computer Science 232(1-2), 165–186 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Salvati, S.: Problèmes de Filtrage et Problèmes d’analyse pour les Grammaires Catégorielles Abstraites. PhD thesis, L’Institut National Polytechnique de Lorraine (2005)Google Scholar
  16. 16.
    Kanazawa, M.: Parsing and generation as Datalog queries. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pp. 176–183 (2007)Google Scholar
  17. 17.
    Clark, A., Eyraud, R., Habrard, A.: Using contextual representations to efficiently learn context-free languages. Journal of Machine Learning Research 11, 2707–2744 (2010)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Clark, A.: Distributional learning of some context-free languages with a minimally adequate teacher [19], pp. 24–37Google Scholar
  19. 19.
    Sempere, J.M., García, P. (eds.): ICGI 2010. LNCS, vol. 6339. Springer, Heidelberg (2010)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ryo Yoshinaka
    • 1
  • Makoto Kanazawa
    • 2
  1. 1.ERATO MINATO Discrete Structure Manipulation System ProjectJapan Science and Technology AgencyJapan
  2. 2.National Institute of InformaticsJapan

Personalised recommendations