Integration of the Dual Approaches in the Distributional Learning of Context-Free Grammars

  • Ryo Yoshinaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7183)


Recently several “distributional learning algorithms” have been proposed and have made great success in learning different subclasses of context-free grammars. The distributional learning models and exploits the relation between strings and contexts that form grammatical sentences in the language of the learning target. There are two main approaches. One, which we call primal, constructs nonterminals whose language is supposed to be characterized by strings. The other, which we call dual, uses contexts to characterize the language of each nonterminal of the conjecture grammar. This paper shows how those opposite approaches are integrated into single learning algorithms that learn quite rich classes of context-free grammars.


Dual Approach Derivation Tree Membership Query Nonterminal Symbol Learning Target 
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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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ryo Yoshinaka
    • 1
  1. 1.Kyoto UniversityJapan

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