Paper: Bagging And Boosting A Treebank Parser

ACL ID A00-2005
Title Bagging And Boosting A Treebank Parser
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2000

Bagging and boosting, two effective machine learn- ing techniques, are applied to natural language pars- ing. Experiments using these techniques with a trainable statistical parser are described. The best resulting system provides roughly as large of a gain in F-measure as doubling the corpus size. Error analysis of the result of the boosting technique re- veals some inconsistent annotations in the Penn Treebank, suggesting a semi-automatic method for finding inconsistent treebank annotations.