Learning Cover Context-Free Grammars from Structural Data

  • Mircea Marin
  • Gabriel Istrate
Conference paper

DOI: 10.1007/978-3-319-10882-7_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8687)
Cite this paper as:
Marin M., Istrate G. (2014) Learning Cover Context-Free Grammars from Structural Data. In: Ciobanu G., Méry D. (eds) Theoretical Aspects of Computing – ICTAC 2014. ICTAC 2014. Lecture Notes in Computer Science, vol 8687. Springer, Cham

Abstract

We consider the problem of learning an unknown context-free grammar when the only knowledge available and of interest to the learner is about its structural descriptions with depth at most ℓ. The goal is to learn a cover context-free grammar (CCFG) with respect to ℓ, that is, a CFG whose structural descriptions with depth at most ℓ agree with those of the unknown CFG. We propose an algorithm, called LA, that efficiently learns a CCFG using two types of queries: structural equivalence and structural membership. We show that LA runs in time polynomial in the number of states of a minimal deterministic finite cover tree automaton (DCTA) with respect to ℓ. This number is often much smaller than the number of states of a minimum deterministic finite tree automaton for the structural descriptions of the unknown grammar.

Keywords

automata theory and formal languages structural descriptions grammatical inference 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mircea Marin
    • 1
  • Gabriel Istrate
    • 1
    • 2
  1. 1.Department of Computer ScienceWest University of TimişoaraTimişoaraRomania
  2. 2.e-Austria Research InstituteTimişoaraRomania

Personalised recommendations