Boosting-based parse reranking with subtree features

Taku Kudo, Jun Suzuki, Hideki Isozaki

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

35 Citations (Scopus)

Abstract

This paper introduces a new application of boosting for parse reranking. Several parsers have been proposed that utilize the all-subtrees representation (e.g., tree kernel and data oriented parsing). This paper argues that such an all-subtrees representation is extremely redundant and a comparable accuracy can be achieved using just a small set of subtrees. We show how the boosting algorithm can be applied to the all-subtrees representation and how it selects a small and relevant feature set efficiently. Two experiments on parse reranking show that our method achieves comparable or even better performance than kernel methods and also improves the testing efficiency.

Original languageEnglish
Title of host publicationACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages189-196
Number of pages8
ISBN (Print)1932432515, 9781932432510
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event43rd Annual Meeting of the Association for Computational Linguistics, ACL-05 - Ann Arbor, MI, United States
Duration: 2005 Jun 252005 Jun 30

Publication series

NameACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Other

Other43rd Annual Meeting of the Association for Computational Linguistics, ACL-05
Country/TerritoryUnited States
CityAnn Arbor, MI
Period05/6/2505/6/30

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Fingerprint

Dive into the research topics of 'Boosting-based parse reranking with subtree features'. Together they form a unique fingerprint.

Cite this