General perspective on distributionally learnable classes

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

2 Citations (Scopus)

Abstract

Several algorithms have been proposed to learn different subclasses of context-free grammars based on the idea generically called distributional learning. Those techniques have been applied to many formalisms richer than context-free grammars like multiple context-free grammars, simple context-free tree grammars and others. The learning algorithms for those different formalisms are actually quite similar to each other. We in this paper give a uniform view on those algorithms.

Original languageEnglish
Title of host publicationMoL 2015 - 14th Meeting on the Mathematics of Language, Proceedings
EditorsMarco Kuhlmann, Makoto Kanazawa, Gregory M. Kobele
PublisherAssociation for Computational Linguistics (ACL)
Pages87-98
Number of pages12
ISBN (Electronic)9781941643563
Publication statusPublished - 2015
Event14th Meeting on the Mathematics of Language, MoL 2015 - Chicago, United States
Duration: 2015 Jul 252015 Jul 26

Publication series

NameMoL 2015 - 14th Meeting on the Mathematics of Language, Proceedings

Conference

Conference14th Meeting on the Mathematics of Language, MoL 2015
Country/TerritoryUnited States
CityChicago
Period15/7/2515/7/26

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science Applications
  • Linguistics and Language

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