Distributional learning of simple context-free tree grammars

Anna Kasprzik, Ryo Yoshinaka

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

13 Citations (Scopus)

Abstract

This paper demonstrates how existing distributional learning techniques for context-free grammars can be adapted to simple context-free tree grammars in a straightforward manner once the necessary notions and properties for string languages have been redefined for trees. Distributional learning is based on the decomposition of an object into a substructure and the remaining structure, and on their interrelations. A corresponding learning algorithm can emulate those relations in order to determine a correct grammar for the target language.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 22nd International Conference, ALT 2011, Proceedings
Pages398-412
Number of pages15
DOIs
Publication statusPublished - 2011 Oct 20
Externally publishedYes
Event22nd International Conference on Algorithmic Learning Theory, ALT 2011 - Espoo, Finland
Duration: 2011 Oct 52011 Oct 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6925 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Algorithmic Learning Theory, ALT 2011
Country/TerritoryFinland
CityEspoo
Period11/10/511/10/7

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Distributional learning of simple context-free tree grammars'. Together they form a unique fingerprint.

Cite this