Paper: Tree Revision Learning for Dependency Parsing

ACL ID N07-1049
Title Tree Revision Learning for Dependency Parsing
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

We present a revision learning model for improving the accuracy of a dependency parser. The revision stage corrects the out- put of the base parser by means of revi- sion rules learned from the mistakes of the base parser itself. Revision learning is performed with a discriminative classi- fier. The revision stage has linear com- plexity and preserves the efficiency of the base parser. We present empirical evalu- ations on the treebanks of two languages, which show effectiveness in relative error reduction and state of the art accuracy.