Distributional learning of simple context-free tree grammars

  • Authors:
  • Anna Kasprzik;Ryo Yoshinaka

  • Affiliations:
  • University of Trier, FB IV Informatik, Trier;Japan Science and Technology Agency

  • Venue:
  • ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
  • Year:
  • 2011

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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.