Distributional learning of conjunctive grammars and contextual binary feature grammars

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2 Citations (Scopus)

Abstract

Approaches based on the idea generically called distributional learning have been making great success in the algorithmic learning of several rich subclasses of context-free languages and their extensions. Those language classes are defined by properties concerning string-context relation. In this paper, we present a distributional learning algorithm for conjunctive grammars with the k-finite context property (k-FCP) for each natural number k. We also compare our result with the closely related work by Clark et al. (JMLR 2010) [5] on learning some context-free grammars using contextual binary feature grammars (CBFGs). We prove that the context-free grammars targeted by their algorithm have the k-FCP. Moreover, we show that every exact CBFG has the k-FCP, too, while not all of them are learnable by their algorithm. Clark et al. conjectured a learning algorithm for exact CBFGs should exist. This paper answers their conjecture in a positive way.

Original languageEnglish
Pages (from-to)359-374
Number of pages16
JournalJournal of Computer and System Sciences
Volume104
DOIs
Publication statusPublished - 2019 Sep
Externally publishedYes

Keywords

  • Conjunctive grammars
  • Context-free grammars
  • Contextual binary feature grammars
  • Distributional learning
  • Grammatical inference
  • Learning theory

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Applied Mathematics

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