Distributional Learning of Simple Context-Free Tree Grammars

  • Anna Kasprzik
  • Ryo Yoshinaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6925)

Abstract

This paper demonstrates how existing distributional learning techniques for context-free grammars can be adapted to simple context-free tree grammars in a straightforward manner once the necessary notions and properties for string languages have been redefined for trees. Distributional learning is based on the decomposition of an object into a substructure and the remaining structure, and on their interrelations. A corresponding learning algorithm can emulate those relations in order to determine a correct grammar for the target language.

Keywords

Positive Data Tree Language Membership Query Grammatical Inference Tree Grammar 
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 2011

Authors and Affiliations

  • Anna Kasprzik
    • 1
  • Ryo Yoshinaka
    • 2
  1. 1.FB IV InformatikUniversity of TrierTrier
  2. 2.ERATO MINATO ProjectJapan Science and Technology AgencyJapan

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