The Strong, Weak, and Very Weak Finite Context and Kernel Properties

  • Makoto KanazawaEmail author
  • Ryo Yoshinaka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10168)


We identify the properties of context-free grammars that exactly correspond to the behavior of the dual and primal versions of Clark and Yoshinaka’s distributional learning algorithm and call them the very weak finite context/kernel property. We show that the very weak finite context property does not imply Yoshinaka’s weak finite context property, which has been assumed to hold of the target language for the dual algorithm to succeed. We also show that the weak finite context property is genuinely weaker than Clark’s strong finite context property, settling a question raised by Yoshinaka.


Grammatical inference and algorithmic learning Distributional learning Finite context property Finite kernel property Context-free languages 


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Institute of Informatics and SOKENDAITokyoJapan
  2. 2.Graduate School of Information SciencesTohoku UniversitySendaiJapan

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