EXPLOITING SUBTREES IN AUTO-PARSED DATA TO IMPROVE DEPENDENCY PARSING
Dependency parsing has attracted considerable interest from researchers and developers in natural language processing. However, to obtain a high-accuracy dependency parser, supervised techniques require a large volume of hand-annotated data, which are extremely expensive. This paper presents a simple and effective approach for improving dependency parsing with subtrees derived from unannotated data, which are easy to obtain. 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. Next, the extracted subtrees are classified into several sets according to their frequency. Finally, we design new features based on the subtree sets for parsing algorithms. To demonstrate the effectiveness of our proposed approach, we conduct experiments on the English Penn Treebank and Chinese Penn Treebank. The results show that our approach significantly outperforms baseline systems. It also achieves the best accuracy for the Chinese data and an accuracy competitive with the best known systems for the English data.
dependency parsing natural language processing semi-supervised learning subtree extraction