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Abstract

The field of Grammatical Inference was originally motivated by the problem of natural language acquisition. However, the formal models proposed within this field have left aside this linguistic motivation. In this paper, we propose to improve models and techniques used in Grammatical Inference by using ideas coming from linguistic studies. In that way, we try to give a new bio-inspiration to this field.

Keywords

Natural Language Language Learning Language Acquisition Negative Data Negative Evidence 
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.
    Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75, 87–106 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Angluin, D., Becerra-Bonache, L.: A model of semantics and corrections in language learning. In: YALEU/DCS/TR-1425 ( April 2010)Google Scholar
  3. 3.
    Angluin, D., Becerra Bonache, L.: Experiments with an algorithm to learn meaning before syntax. In: ForLing2008, pp. 1–12 (2008)Google Scholar
  4. 4.
    Angluin, D., Becerra-Bonache, L.: Learning meaning before syntax. In: Clark, A., Coste, F., Miclet, L. (eds.) ICGI 2008. LNCS (LNAI), vol. 5278, pp. 1–14. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Becerra-Bonache, L.: On the Learnability of Mildly Context-Sensitive Languages using Positive Data and Correction Queries. PhD thesis, Rovira i Virgili University (2006)Google Scholar
  6. 6.
    Becerra-Bonache, L., Case, J., Jain, S., Stephan, F.: Iterative learning of simple external contextual languages. In: Freund, Y., Györfi, L., Turán, G., Zeugmann, T. (eds.) ALT 2008. LNCS (LNAI), vol. 5254, pp. 359–373. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Becerra-Bonache, L., de la Higuera, C., Janodet, J.C., Tantini, F.: Learning balls of strings from edit corrections. Journal of Machine Learning Research 9, 1841–1870 (2008)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Becerra-Bonache, L., Dediu, A.-H., Tîrnăucă, C.: Learning DFA from correction and equivalence queries. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds.) ICGI 2006. LNCS (LNAI), vol. 4201, pp. 281–292. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Becerra-Bonache, L., Yokomori, T.: Learning mild context-sensitiveness: Toward understanding children’s language learning. In: Paliouras, G., Sakakibara, Y. (eds.) ICGI 2004. LNCS (LNAI), vol. 3264, pp. 53–64. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Bresnan, J., Kaplan, R.M., Peters, S., Zaenen, A.: Cross-serial dependencies in dutch. In: Savitch, W.J., Bach, E., Marsh, W., Safran-Naveh, G. (eds.) The Formal Complexity of Natural Language, pp. 286–319. D. Reidel, Dordrecht (1987)Google Scholar
  11. 11.
    Brown, R., Hanlon, C.: Derivational complexity and order of acquisition in child speech. In: Hayes, J.R. (ed.) Cognition and the Development of Language, pp. 11–54. Wiley, New York (1970)Google Scholar
  12. 12.
    Chouinard, M.M., Clark, E.V.: Adult reformulations of child errors as negative evidence. Journal of Child Language 30, 637–669 (2003)CrossRefGoogle Scholar
  13. 13.
    Clark, A.: Grammatical inference and first language acquisition. In: Psychocomputational Models of Human Language Acquisition, Geneva, pp. 25–32 (2004)Google Scholar
  14. 14.
    Clark, E.V.: First Language Acquistion. Cambridge University Press, Cambridge (2002)Google Scholar
  15. 15.
    Culy, C.: The complexity of the vocabulary of bambara. In: Savitch, W.J., Bach, E., Marsh, W., Safran-Naveh, G. (eds.) The Formal Complexity of Natural Language, pp. 349–357 (1987)Google Scholar
  16. 16.
    de la Higuera, C.: A bibliographical study of grammatical inference. Pattern Recognition 38, 1332–1348 (2005)CrossRefGoogle Scholar
  17. 17.
    de la Higuera, C.: Grammatical inference: learning automata and grammars. Cambridge University Press, Cambridge (2010)CrossRefzbMATHGoogle Scholar
  18. 18.
    Gold, E.M.: Language identification in the limit. Information and Control 10, 447–474 (1967)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Hirsh-Pasek, K., Treiman, R.A., Schneiderman, M.: Brown and hanlon revisited: mothers sensitivity to ungrammatical forms. Journal of Child Language 11, 81–88 (1984)CrossRefGoogle Scholar
  20. 20.
    Joshi, A.K.: How much context-sensitivity is required to provide reasonable structural descriptions: Tree adjoining grammars. In: Dowty, D., Karttunen, L., Zwicky, A. (eds.) Natural Language Parsing: Psychological, Computational and Theoretical Perspectives, pp. 206–250. Cambridge University Press, Cambridge (1985)CrossRefGoogle Scholar
  21. 21.
    Kudlek, M., Martín-Vide, C., Mateescu, A., Mitrana, V.: Contexts and the concept of mild context-sensitivity. Linguistics and Philosophy 26(6), 703–725 (2002)CrossRefGoogle Scholar
  22. 22.
    Manaster-Ramer, A.: Some uses and abuses of mathematics in linguistics. In: Martín-Vide, C. (ed.) Issues in Mathematical Linguistics, pp. 73–130. John Benjamins, Amsterdam (1999)CrossRefGoogle Scholar
  23. 23.
    Marcus, G.F.: Negative evidence in language acquisition. Cognition 46, 53–95 (1993)CrossRefGoogle Scholar
  24. 24.
    Morgan, J.L., Travis, L.L.: Limits on negative information in language input. Journal of Child Language 16, 531–552 (1989)CrossRefGoogle Scholar
  25. 25.
    Oates, T., Armstrong, T., Bonache, L.B., Atamas, M.: Inferring grammars for mildly context sensitive languages in polynomial-time. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds.) ICGI 2006. LNCS (LNAI), vol. 4201, pp. 137–147. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  26. 26.
    Parekh, R., Honavar, V.: Grammar inference, automata induction and language acquisition. In: Moisl Dale and Somers, editors, pp. 727–774. Marcel Dekker, New York (2000)Google Scholar
  27. 27.
    Pinker, S.: Formal models of language learning. Cognition 7, 217–283 (1979)CrossRefGoogle Scholar
  28. 28.
    Sakakibara, Y.: Recent advances of grammatical inference. Theoretical Computer Science 185, 15–45 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Schaerlaekens, A.M.: The two-word sentence in child language development. In: Mouton, The Hague (1973)Google Scholar
  30. 30.
    Schlesinger, I.M.: Production of utterances and language acquisition. In: Slobin, D.I. (ed.) The Ontogenesis of Grammar, pp. 63–103. Academic Press, New York-London (1971)Google Scholar
  31. 31.
    Shieber, S.M.: Evidence against the context-freeness of natural languages. In: Savitch, W.J., Bach, E., Marsh, W., Safran-Naveh, G. (eds.) The Formal Complexity of Natural Language, pp. 320–334. D. Reidel, Dordrecht (1987)Google Scholar
  32. 32.
    Valiant, L.G.: A theory of the learnable. Communication of the ACM 27, 1134–1142 (1984)CrossRefzbMATHGoogle Scholar
  33. 33.
    Wexler, K., Culicover, P.: Formal Principles of Languages Acquisition. MIT Press, Cambridge (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Leonor Becerra-Bonache
    • 1
  1. 1.Laboratoire Hubert Curien, UMR CNRS 5516Université de Saint-Etienne, Jean MonnetSaint-EtienneFrance

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