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Bagging and Boosting a Treebank Parser

John C. Henderson·Eric Brill
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Abstract

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.

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John C. Henderson, Eric Brill. 2000-06-05. Bagging and Boosting a Treebank Parser. https://arxiv.org/abs/cs/0006011

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