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Bagging and Boosting a Treebank Parser | | John C. Henderson
; Eric Brill
; | | Date: |
5 Jun 2000 | | Journal: | Proceedings of the 1st Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL-2000), pages 34-41 | | Subject: | Computation and Language ACM-class: I.2.7 | cs.CL | | 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. | | Source: | arXiv, cs.CL/0006011 | | Services: | Forum | Review | PDF | Favorites | |
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