Integration of the dual approaches in the distributional learning of context-free grammars

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

10 Citations (Scopus)

Abstract

Recently several "distributional learning algorithms" have been proposed and have made great success in learning different subclasses of context-free grammars. The distributional learning models and exploits the relation between strings and contexts that form grammatical sentences in the language of the learning target. There are two main approaches. One, which we call primal, constructs nonterminals whose language is supposed to be characterized by strings. The other, which we call dual, uses contexts to characterize the language of each nonterminal of the conjecture grammar. This paper shows how those opposite approaches are integrated into single learning algorithms that learn quite rich classes of context-free grammars.

Original languageEnglish
Title of host publicationLanguage and Automata Theory and Applications - 6th International Conference, LATA 2012, Proceedings
Pages538-550
Number of pages13
DOIs
Publication statusPublished - 2012 Mar 12
Externally publishedYes
Event6th International Conference on Language and Automata Theory and Applications, LATA 2012 - A Coruna, Spain
Duration: 2012 Mar 52012 Mar 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7183 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Language and Automata Theory and Applications, LATA 2012
CountrySpain
CityA Coruna
Period12/3/512/3/9

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Integration of the dual approaches in the distributional learning of context-free grammars'. Together they form a unique fingerprint.

Cite this