Dependency Parsing with Short Dependency Relations in Unlabeled Data

This paper presents an effective dependency parsing approach of incorporating short dependency information from unlabeled data. The unlabeled data is automatically parsed by a deterministic dependency parser, which can provide relatively high performance for short dependencies between words. We then train another parser which uses the information on short dependency relations extracted from the output of the first parser. Our proposed approach achieves an unlabeled attachment score of 86.52, an absolute 1.24% improvement over the baseline system on the data set of Chinese Treebank.