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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)

Abstract

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.

Keywords

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.

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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

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