Abstract
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.
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Wu, F., Zhou, F. (2015). Hybrid Dependency Parser with Segmented Treebanks and Reparsing. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_6
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DOI: https://doi.org/10.1007/978-3-662-46469-4_6
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