Learning context-free grammars from structural data in polynomial time

Research output: Contribution to journalArticle

88 Citations (Scopus)

Abstract

We consider the problem of learning a context-free grammar from its structural descriptions. Structural descriptions of a context-free grammar are unlabelled derivation trees of the grammar. We present an efficient algorithm for learning context-free grammars using two types of queries: structural equivalence queries and structural membership queries. The learning protocol is based on what is called "minimally adequate teacher", and it is shown that a grammar learned by the algorithm is not only a correct grammar, i.e. equivalent to the unknown grammar but also structurally equivalent to it. Furthermore, the algorithm runs in time polynomial in the number of states of the minimum frontier-to-root tree automaton for the set of structural descriptions of the unknown grammar and the maximum size of any counter-example returned by a structural equivalence query.

Original languageEnglish
Pages (from-to)223-242
Number of pages20
JournalTheoretical Computer Science
Volume76
Issue number2-3
DOIs
Publication statusPublished - 1990 Nov 21
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Learning context-free grammars from structural data in polynomial time'. Together they form a unique fingerprint.

  • Cite this