Efficient learning of multiple context-free languages with multidimensional substitutability from positive data

  • Authors:
  • Ryo Yoshinaka

  • Affiliations:
  • -

  • Venue:
  • Theoretical Computer Science
  • Year:
  • 2011

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Abstract

Recently Clark and Eyraud (2007) [10] have shown that substitutable context-free languages, which capture an aspect of natural language phenomena, are efficiently identifiable in the limit from positive data. Generalizing their work, this paper presents a polynomial-time learning algorithm for new subclasses of multiple context-free languages with variants of substitutability.