Hybrid Dependency Parser with Segmented Treebanks and Reparsing

We propose a hybrid dependency parsing pipeline which combines transition-based parser and graph-based parser, and use segmented treebanks to train transition-based parsers as subparsers in front end, and then propose a constrained Eisner’s algorithm to reparse their outputs. We build the pipeline to investigate the influence on parsing accuracy when training with different segmentations of training data and find a convenient method to obtain parsing reliability score while achieving state-of-the-art parsing accuracy. Our results show that the pipeline with segmented training dataset could improve accuracy through reparsing while providing parsing reliability score.