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NAACL
2007

Tree Revision Learning for Dependency Parsing

9 years 3 months ago
Tree Revision Learning for Dependency Parsing
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
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NAACL
Authors Giuseppe Attardi, Massimiliano Ciaramita
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