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EMNLP
2009

Improving Dependency Parsing with Subtrees from Auto-Parsed Data

8 years 4 months ago
Improving Dependency Parsing with Subtrees from Auto-Parsed Data
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 extract subtrees from dependency parse trees in the auto-parsed data. Finally, we construct new subtree-based features for parsing algorithms. To demonstrate the effectiveness of our proposed approach, we present the experimental results on the English Penn Treebank and the Chinese Penn Treebank. These results show that our approach significantly outperforms baseline systems. And, it achieves the best accuracy for the Chinese data and an accuracy which is competitive with the best known systems for the English data.
Wenliang Chen, Jun'ichi Kazama, Kiyotaka Uchimoto,
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where EMNLP
Authors Wenliang Chen, Jun'ichi Kazama, Kiyotaka Uchimoto, Kentaro Torisawa
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