Towards dual approaches for learning context-free grammars based on syntactic concept lattices

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

20 Citations (Scopus)

Abstract

Recent studies on grammatical inference have demonstrated the benefits of "distributional learning" for learning context-free and context-sensitive languages. Distributional learning models and exploits the relation between strings and contexts in the language of the learning target. There are two main approaches. One, which we call primal, constructs nonterminals whose language is characterized by strings. The other, which we call dual, uses contexts to characterize the language of a nonterminal of the conjecture grammar. This paper demonstrates and discusses the duality of those approaches by presenting some powerful learning algorithms along the way.

Original languageEnglish
Title of host publicationDevelopments in Language Theory - 15th International Conference, DLT 2011, Proceedings
Pages429-440
Number of pages12
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event15th International Conference on Developments in Language Theory, DLT 2011 - Milan, Italy
Duration: 2011 Jul 192011 Jul 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6795 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Developments in Language Theory, DLT 2011
CountryItaly
CityMilan
Period11/7/1911/7/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Towards dual approaches for learning context-free grammars based on syntactic concept lattices'. Together they form a unique fingerprint.

Cite this