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Learning n-Ary Node Selecting Tree Transducers from Completely Annotated Examples

  • A. Lemay
  • J. Niehren
  • R. Gilleron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4201)

Abstract

We present the first algorithm for learning n-ary node selection queries in trees from completely annotated examples by methods of grammatical inference. We propose to represent n-ary queries by deterministic n-ary node selecting tree transducers (n-NSTTs). These are tree automata that capture the class of monadic second-order definable n-ary queries. We show that n-NSTTs defined polynomially bounded n-ary queries can be learned from polynomial time and data. An application in Web information extraction yields encouraging results.

Keywords

Polynomial Time Tree Automaton Tree Language Tree Transducer Deterministic Automaton 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Lemay
    • 1
  • J. Niehren
    • 2
  • R. Gilleron
    • 1
  1. 1.Mostrare project of INRIA Futurs, LIFLUniversity of Lille 3LilleFrance
  2. 2.Mostrare project of INRIA Futurs, LIFLINRIA FutursLilleFrance

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