Learning, Agents, and Formal Languages: Linguistic Applications of Interdisciplinary Fields

  • Leonor Becerra-Bonache
  • M. Dolores Jiménez-López
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 372)

Abstract

This paper focuses on three areas: machine learning, agent technologies and formal language theory. Our goal is to show how the interrelation among agents, learning and formal languages can contribute to the solution of a challenging problem: the explanation of how natural language is acquired and processed. Linguistic contributions of the intersection between machine learning and formal language theory –through the field of grammatical inference– are reviewed. Agent-based formal language models as colonies, grammar systems and eco-grammar systems have been applied to different natural language issues. We review the most relevant applications of these models.

Keywords

Natural Language Multiagent System Language Learning Formal Language Machine Translation 
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.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adriaans, P.: Language learning from a categorial perspective. PhD thesis, University of Amsterdam (1992)Google Scholar
  2. 2.
    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
  3. 3.
    Angluin, D., Becerra-Bonache, L.: Effects of meaning-preserving corrections on language learning. In: CoNLL 2011, pp. 97–105 (2011)Google Scholar
  4. 4.
    Angluin, D., Becerra-Bonache, L.: A Model of semantics and corrections in language learning. Technical Report, Yale University, 1–45 (2010)Google Scholar
  5. 5.
    Becerra-Bonache, L., Case, J., Jain, S., Stephan, F.: Iterative learning of simple external contextual languages. Theoretical Computer Science 411, 2741–2756 (2010)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Bel-Enguix, G., Jiménez-López, M.D.: Modelling dialogue as inter-action. International Journal of Speech Technology 11(3/4), 209–221 (2008)CrossRefGoogle Scholar
  7. 7.
    Bel-Enguix, G., Jiménez-López, M.D., Martín-Vide, C.: Using finite-state methods for getting infinite languages: A preview. Romanian Journal of Information, Science and Technology 12(2), 125–137 (2009)Google Scholar
  8. 8.
    Brooks, R.A.: Elephants don’t play chess. Robotics and Autonomous Systems 6, 3–15 (1990)CrossRefGoogle Scholar
  9. 9.
    Casacuberta, F., Vidal, E.: Learning finite-state models for machine translation. Machine Learning 66(1), 69–91 (2007)CrossRefGoogle Scholar
  10. 10.
    Chouinard, M.M., Clark, E.V.: Adult reformulations of child errors as negative evidence. Journal of Child Language 30, 637–669 (2003)CrossRefGoogle Scholar
  11. 11.
    Csuhaj-Varjú, E., Dassow, J., Kelemen, J., Păun, G.: Grammar systems: A grammatical approach to distribution and cooperation. Gordon and Breach, London (1994)MATHGoogle Scholar
  12. 12.
    Csuhaj-Varjú, E., Kelemen, J., Kelemenová, A., Păun, G.: Eco-grammar systems: A grammatical framework for life-like interactions. Artificial Life 3(1), 1–28 (1996)CrossRefGoogle Scholar
  13. 13.
    de la Higuera, C.: Grammatical inference: Learning automata and grammars. Cambridge University Press, Cambridge (2010)CrossRefGoogle Scholar
  14. 14.
    D‘Ulizia, A., Ferri, F., Grifoni, P.: A survey of grammatical inference methods for natural language learning. Artificial Intelligence Review 36(1), 1–27 (2011)CrossRefGoogle Scholar
  15. 15.
    Gold, E.M.: Language identification in the limit. Information and Control 10, 447–474 (1967)CrossRefMATHGoogle Scholar
  16. 16.
    Jiménez-López, M.D.: A grammar systems approach to natural language grammar. Linguistics and Philosophy 29, 419–454 (2006)CrossRefGoogle Scholar
  17. 17.
    Kelemen, J., Kelemenová, A.: A grammar-theoretic treatment of multiagent systems. Cybernetics and Systems 23, 621–633 (1992)MATHMathSciNetGoogle Scholar
  18. 18.
    Sadock, J.M.: Autolexical syntax. A theory of parallel grammatical representations. University of Chicago Press, Chicago (1991)Google Scholar
  19. 19.
    Solan, Z., Horn, D., Ruppin, E., Edelman, S.: Unsupervised learning of natural languages. PNAS 102(33), 11629–11634 (2005)Google Scholar
  20. 20.
    van Zaanen, M.: Bootstrapping structure into language: alignment-based learning. PhD thesis, University of Leeds (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leonor Becerra-Bonache
    • 1
  • M. Dolores Jiménez-López
    • 2
  1. 1.Laboratoire Hubert CurienJean Monnet UniversitySaint-EtienneFrance
  2. 2.Research Group on Mathematical LinguisticsUniversitat Rovira i VirgiliTarragonaSpain

Personalised recommendations