Grammar learning by partition search

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

This paper describes Grammar Learning by Partition Search, a general method for automatically constructing grammars for a range of parsing tasks. Given a base grammar, a training corpus, and a parsing task, Partition Search constructs an optimised probabilistic context-free grammar by searching a space of nonterminal set partitions, looking for a partition that maximises parsing performance and minimises grammar size. The method can be used to optimise grammars in terms of size and performance, or to adapt existing grammars to new parsing tasks and new domains. This paper reports an example application to optimising a base grammar extracted from the Wall Street Journal Corpus. Partition Search improves parsing performance by up to 5.29%, and reduces grammar size by up to 16.89%. Parsing results are better than in existing treebank grammar research, and compared to other grammar compression methods, Partition Search has the advantage of achieving compression without loss of grammar coverage.
Original languageEnglish
Title of host publicationProceedings of the LREC 2002 workshop on event modelling for multilingual document linking
Place of PublicationAmsterdam/Philadelphia
PublisherJohn Benjamins Publishing Company
Pages0-0
Number of pages1
Publication statusPublished - 1 Jan 2002
EventProceedings of the LREC 2002 workshop on event modelling for multilingual document linking - Las Palmas, Spain
Duration: 1 Jan 2002 → …

Workshop

WorkshopProceedings of the LREC 2002 workshop on event modelling for multilingual document linking
Period1/01/02 → …

Keywords

  • Natural language generation
  • Partition searching

Fingerprint Dive into the research topics of 'Grammar learning by partition search'. Together they form a unique fingerprint.

  • Cite this

    Belz, A. (2002). Grammar learning by partition search. In Proceedings of the LREC 2002 workshop on event modelling for multilingual document linking (pp. 0-0). John Benjamins Publishing Company.