Dependency Parser Adaptation with Subtrees from Auto-Parsed Target Domain Data

Xuezhe Ma, Fei Xia

Abstract: In this paper, we propose a simple and effective approach to domain adaptation for dependency parsing. This is a feature augmentation approach in which the new features are constructed based on subtree information extracted from the autoparsed target domain data. To demonstrate the effectiveness of the proposed approach, we evaluate it on three pairs of source-target data, compared with several common baseline systems and previous approaches. Our approach achieves significant improvement on all the three pairs of data sets.