Bagging and Boosting a Treebank Parser

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.

• Type: Conference
• Year: 2000
• Where: ANLP
• Authors: John C. Henderson, Eric Brill
• Keywords: ANLP 2000, Inconsistent Treebank Annotations, Machine Learning Techniques, Trainable Statistical Parser