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28

NAACL
2007

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Tree Revision Learning for Dependency Parsing

14 years 9 days ago

Tree Revision Learning for Dependency Parsing

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We present a revision learning model for improving the accuracy of a dependency parser. The revision stage corrects the output of the base parser by means of revision rules learned from the mistakes of the base parser itself. Revision learning is performed with a discriminative classiﬁer. The revision stage has linear complexity and preserves the efﬁciency of the base parser. We present empirical evaluations on the treebanks of two languages, which show effectiveness in relative error reduction and state of the art accuracy.

Giuseppe Attardi, Massimiliano Ciaramita

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Base Parser | Computational Linguistics | NAACL 2007 | Revision Learning | Revision Stage |

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Added	30 Oct 2010
Updated	30 Oct 2010
Type	Conference
Year	2007
Where	NAACL
Authors	Giuseppe Attardi, Massimiliano Ciaramita

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