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Learning Mildly Context-Sensitive Languages with Multidimensional Substitutability from Positive Data

  • Ryo Yoshinaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5809)

Abstract

Recently Clark and Eyraud (2007) 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 mildly context-sensitive languages with variants of substitutability.

Keywords

Target Language Regular Language Positive Data Tree Transducer Short String 
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 2009

Authors and Affiliations

  • Ryo Yoshinaka
    • 1
  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

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