  Bagging and boosting, two effective machine learning techniques, are applied
to natural language parsing. 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 reveals some inconsistent
annotations in the Penn Treebank, suggesting a semi-automatic method for
finding inconsistent treebank annotations.
