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.
Instead of using specialized semi-supervised learning algorithms, REF used features based on sub-structures in auto-parsed data and demonstrated the effectiveness of these features.
Improving Dependency Parsing with Subtrees from Auto-Parsed Data
EMNLP
Computer Science