The strong, weak, and very weak finite context and kernel properties

Makoto Kanazawa, Ryo Yoshinaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLanguage and Automata Theory and Applications - 11th International Conference, LATA 2017, Proceedings
EditorsFrank Drewes, Carlos Martín-Vide, Bianca Truthe
PublisherSpringer-Verlag
Pages77-88
Number of pages12
ISBN (Print)9783319537320
DOIs
Publication statusPublished - 2017 Jan 1
Event11th International Conference on Language and Automata Theory and Applications, LATA 2017 - Umea, Sweden
Duration: 2017 Mar 62017 Mar 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10168 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Language and Automata Theory and Applications, LATA 2017
CountrySweden
City Umea
Period17/3/617/3/9

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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