Efficient Parsing Using Recursive Transition Networks with Output

  • Javier M. Sastre-Martínez
  • Mikel L. Forcada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5603)

Abstract

We describe here two efficient parsing algorithms for natural language texts based on an extension of recursive transition networks (RTN) called recursive transition networks with string output (RTNSO). RTNSO-based grammars can be semiautomatically built from samples of a manually built syntactic lexicon. Efficient parsing algorithms are needed to minimize the temporal cost associated to the size of the resulting networks. We focus our algorithms on the RTNSO formalism due to its simplicity which facilitates the manual construction and maintenance of RTNSO-based linguistic data as well as their exploitation.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Woods, W.A.: Transition network grammars for natural language analysis. Commun. ACM 13(10), 591–606 (1970)CrossRefMATHGoogle Scholar
  2. 2.
    Gross, M.: Méthodes en Syntaxe. Hermann, Paris (1975)Google Scholar
  3. 3.
    Gross, M.: Lexicon Grammar, pp. 244–258. Pergamon Press, Cambridge (1996)Google Scholar
  4. 4.
    Sastre, J.M.: HOOP: a Hyper-Object Oriented Platform for the management of linguistic databases. In: Commun. of the Lexis and Grammar Conference 2006 (2006)Google Scholar
  5. 5.
    Roche, E.: Analyse syntaxique transformationnelle du français par transducteurs et lexique-grammaire. PhD thesis, Université Paris 7, Paris (1993)Google Scholar
  6. 6.
    Nakamura, T.: Analyse automatique d’un discours spcialis au moyen de grammaires locales. In: Purnelle, G., Fairn, C., Dister, A. (eds.) JADT 2004, Louvain-la-Neuve, pp. 837–847. UCL Presses universitaires de Louvain (2004)Google Scholar
  7. 7.
    Silberztein, M.D.: Dictionnaires électroniques et analyse automatique de textes. Le systéme INTEX, Masson, Paris (1993)Google Scholar
  8. 8.
    Paumier, S.: Unitex 1.2 User Manual. Université de Marne-la-Vallée (2006)Google Scholar
  9. 9.
    Blanc, O., Constant, M.: Outilex, a linguistic platform for text processing. In: COLING/ACL 2006, Morristown, NJ, USA, pp. 73–76. Association for Computational Linguistics (2006)Google Scholar
  10. 10.
    Garrido-Alenda, A., Forcada, M.L., Carrasco, R.C.: Incremental construction and maintenance of morphological analysers based on augmented letter transducers. In: TMI 2002, pp. 53–62 (2002)Google Scholar
  11. 11.
    Van Noord, G.: Treatment of epsilon moves in subset construction. Comput. Linguist. 26(1), 61–76 (2000)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Fredkin, E.: Trie memory. Communications of the ACM 3(9), 490–499 (1960)CrossRefGoogle Scholar
  13. 13.
    Earley, J.: An efficient context-free parsing algorithm. Commun. ACM 13(2), 94–102 (1970)CrossRefMATHGoogle Scholar
  14. 14.
    Woods, W.A.: Augmented transition networks for natural language analysis. Technical report CS-1, Comput. Lab., Harvard U., Cambridge, Mass. (1969)Google Scholar
  15. 15.
    Aycock, J., Horspool, N.: Practical Earley Parsing. The Computer Journal 45(6), 620–630 (2002)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Javier M. Sastre-Martínez
    • 1
    • 2
  • Mikel L. Forcada
    • 2
  1. 1.Laboratoire d’Informatique de l’Institut Gaspard MongeUniversité Paris-EstMarne-la-Vallée Cedex 2France
  2. 2.Grup Transducens, Departament de Llenguatges i Sistemes InformàticsUniversitat d’AlacantAlacantSpain

Personalised recommendations