Distributional learning and context/substructure enumerability in nonlinear tree grammars

Makoto Kanazawa, Ryo Yoshinaka

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

Abstract

We study tree-generating almost linear second-order ACGs that admit bounded nonlinearity either on the context side or on the substructure side, and give distributional learning algorithms for them.

Original languageEnglish
Title of host publicationFormal Grammar - 20th and 21st International Conferences FG 2015 and FG 2016, Revised Selected Papers
EditorsSylvain Pogodalla, Reinhard Muskens, Annie Foret, Glyn Morrill, Rainer Osswald
PublisherSpringer Verlag
Pages94-111
Number of pages18
ISBN (Print)9783662530412
DOIs
Publication statusPublished - 2016 Jan 1
Event20th Conference on Formal Grammar, FG 2015 and 21st Conference on Formal Grammar, FG 2016 - Barcelona, Spain
Duration: 2016 Aug 202016 Aug 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9804 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th Conference on Formal Grammar, FG 2015 and 21st Conference on Formal Grammar, FG 2016
CountrySpain
CityBarcelona
Period16/8/2016/8/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kanazawa, M., & Yoshinaka, R. (2016). Distributional learning and context/substructure enumerability in nonlinear tree grammars. In S. Pogodalla, R. Muskens, A. Foret, G. Morrill, & R. Osswald (Eds.), Formal Grammar - 20th and 21st International Conferences FG 2015 and FG 2016, Revised Selected Papers (pp. 94-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9804 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-662-53042-9_6