Unified framework for hybrid dependency Parsin

The mainstream dependency parser is a supervised statistical parser whose performance greatly relies on manually annotated dataset in recently. In order to use multi-treebank without building a new parser, a hybrid dependency processing pipeline is proposed. The pipeline is implemented through maximum spanning tree (MST) algorithm and linear chain conditional random fields (CRF) as base framework, and a hybrid dependency processing pipeline for training the parser by using multi-treebank is constructed, then a composite dependency parser is built from base framework to utilizes cross information of the multi-treebank with a set of hybrid feature templates. The result shows that the pipeline can improve the parsing precision of single-treebank parser without designing a new parser.