Polynomial-Time Identification of Multiple Context-Free Languages from Positive Data and Membership Queries

  • Ryo Yoshinaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6339)


This paper presents an efficient algorithm that identifies a rich subclass of multiple context-free languages in the limit from positive data and membership queries by observing where each tuple of strings may occur in sentences of the language of the learning target. Our technique is based on Clark et al.’s work (ICGI 2008) on learning of a subclass of context-free languages. Our algorithm learns those context-free languages as well as many non-context-free languages.


Target Language Regular Language Positive Data Tree Language Membership Query 
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 2010

Authors and Affiliations

  • Ryo Yoshinaka
    • 1
  1. 1.Minato Discrete Structure Manipulation System ProjectJapan Science and Technology AgencyErato

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