Paper: Improving Dependency Parsing with Subtrees from Auto-Parsed Data

ACL ID D09-1060
Title Improving Dependency Parsing with Subtrees from Auto-Parsed Data
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

This paper presents a simple and effective approach to improve dependency parsing by using subtrees from auto-parsed data. First, we use a baseline parser to parse large-scale unannotated data. Then we ex- tract subtrees from dependency parse trees in the auto-parsed data. Finally, we con- struct new subtree-based features for pars- ing algorithms. To demonstrate the ef- fectiveness of our proposed approach, we present the experimental results on the En- glish Penn Treebank and the Chinese Penn Treebank. These results show that our ap- proach significantly outperforms baseline systems. And, it achieves the best accu- racy for the Chinese data and an accuracy which is competitive with the best known systems for the English data.