Efficient learning of multiple context-free languages with multidimensional substitutability from positive data

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Recently Clark and Eyraud (2007) [10] have shown that substitutable context-free languages, which capture an aspect of natural language phenomena, are efficiently identifiable in the limit from positive data. Generalizing their work, this paper presents a polynomial-time learning algorithm for new subclasses of multiple context-free languages with variants of substitutability.

Original languageEnglish
Pages (from-to)1821-1831
Number of pages11
JournalTheoretical Computer Science
Volume412
Issue number19
DOIs
Publication statusPublished - 2011 Apr 22
Externally publishedYes

Keywords

  • Grammatical inference
  • Mildly context-sensitive grammars
  • Multiple context-free grammars

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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