Learning Sequential Tree-to-Word Transducers

  • Grégoire Laurence
  • Aurélien Lemay
  • Joachim Niehren
  • Sławek Staworko
  • Marc Tommasi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8370)


We study the problem of learning sequential top-down tree-to-word transducers (stws). First, we present a Myhill-Nerode characterization of the corresponding class of sequential tree-to-word transformations (\({\mathcal{STW}}\)). Next, we investigate what learning of stws means, identify fundamental obstacles, and propose a learning model with abstain. Finally, we present a polynomial learning algorithm.


Polynomial Time Learn Sequential Regular Language Context Free Grammar Input Tree 
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.
    Bex, G.J., Gelade, W., Neven, F., Vansummeren, S.: Learning deterministic regular expressions for the inference of schemas from XML data. ACM Transactions on the Web 4(4) (2010)Google Scholar
  2. 2.
    Bex, G.J., Neven, F., Schwentick, T., Vansummeren, S.: Inference of concise regular expressions and DTDs. ACM TODS 35(2) (2010)Google Scholar
  3. 3.
    Carme, J., Gilleron, R., Lemay, A., Niehren, J.: Interactive learning of node selecting tree transducers. Machine Learning 66(1), 33–67 (2007)CrossRefGoogle Scholar
  4. 4.
    Choffrut, C.: Minimizing subsequential transducers: A survey. TCS 292(1), 131–143 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Comon, H., Dauchet, M., Gilleron, R., Löding, C., Jacquemard, F., Lugiez, D., Tison, S., Tommasi, M.: Tree automata techniques and applications (October 2007), Available online since 1997:
  6. 6.
    de la Higuera, C.: A bibliographical study of grammatical inference. Pattern Recognition 38, 1332–1348 (2005)CrossRefGoogle Scholar
  7. 7.
    Engelfriet, J., Maneth, S., Seidl, H.: Deciding equivalence of top-down XML transformations in polynomial time. Journal of Computer and System Science 75(5), 271–286 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Filiot, E., Raskin, J.-F., Reynier, P.-A., Servais, F., Talbot, J.-M.: Properties of visibly pushdown transducers. In: Hliněný, P., Kučera, A. (eds.) MFCS 2010. LNCS, vol. 6281, pp. 355–367. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Friese, S., Seidl, H., Maneth, S.: Minimization of deterministic Bottom-Up tree transducers. In: Gao, Y., Lu, H., Seki, S., Yu, S. (eds.) DLT 2010. LNCS, vol. 6224, pp. 185–196. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Gold, E.M.: Complexity of automaton identification from given data. Inform. Control 37, 302–320 (1978)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Griffiths, T.V.: The unsolvability of the equivalence problem for Lambda-Free nondeterministic generalized machines. Journal of the ACM 15(3), 409–413 (1968)CrossRefzbMATHGoogle Scholar
  12. 12.
    Laurence, G., Lemay, A., Niehren, J., Staworko, S., Tommasi, M.: Normalization of sequential Top-Down Tree-to-Word transducers. In: Dediu, A.-H., Inenaga, S., Martín-Vide, C. (eds.) LATA 2011. LNCS, vol. 6638, pp. 354–365. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Lemay, A., Maneth, S., Niehren, J.: A learning algorithm for Top-Down XML transformations. In: 29th PODS 2010, pp. 285–296. ACM Press (2010)Google Scholar
  14. 14.
    Martens, W., Neven, F., Gyssens, M.: Typechecking top-down XML transformations: Fixed input or output schemas. Inf. Comput. 206(7), 806–827 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Oncina, J., García, P.: Inference of recognizable tree sets. Tech. report, Dept de Sistemas Informáticos y Computación, Univ. de Alicante (1993), DSIC-II/47/93Google Scholar
  16. 16.
    Oncina, J., Gracia, P.: Identifying regular languages in polynomial time. In: Advances in Structural and Syntactic Pattern Recognition, pp. 99–108 (1992)Google Scholar
  17. 17.
    Papadimitriou, C.: Computational complexity. Addison-Wesley (1994)Google Scholar
  18. 18.
    Raskin, J.-F., Servais, F.: Visibly pushdown transducers. In: Aceto, L., Damgård, I., Goldberg, L.A., Halldórsson, M.M., Ingólfsdóttir, A., Walukiewicz, I. (eds.) ICALP 2008, Part II. LNCS, vol. 5126, pp. 386–397. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Staworko, S., Laurence, G., Lemay, A., Niehren, J.: Equivalence of deterministic nested word to word transducers. In: Kutyłowski, M., Charatonik, W., Gębala, M. (eds.) FCT 2009. LNCS, vol. 5699, pp. 310–322. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Staworko, S., Wieczorek, P.: Learning XML twig queries. CoRR, abs/1106.3 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Grégoire Laurence
    • 1
    • 3
  • Aurélien Lemay
    • 1
    • 3
  • Joachim Niehren
    • 1
    • 4
  • Sławek Staworko
    • 1
    • 3
  • Marc Tommasi
    • 2
    • 3
  1. 1.Links project, INRIA & LIFL (CNRS UMR8022)France
  2. 2.Magnet Project, INRIA & LIFL (CNRS UMR8022)France
  3. 3.University of LilleFrance
  4. 4.INRIALilleFrance

Personalised recommendations