DFKI-LT - Hybrid Learning of Dependency Structures from Heterogeneous Linguistic Resources

Yi Zhang, Rui Wang, Hans Uszkoreit
Hybrid Learning of Dependency Structures from Heterogeneous Linguistic Resources
3 Proceedings of the Twelfth Conference on Computational Natural Language Learning, Pages 198-202, Manchester, United Kingdom, Association for Computational Linguistics, 2008
 
In this paper we present our syntactic and semantic dependency parsing system participated in both closed and open competitions of the CoNLL 2008 Shared Task. By combining the outcome of two state-of-the-art syntactic dependency parsers, we achieved high accuracy in syntactic dependencies (87.32%). With MRSes from grammar-based HPSG parsers, we achieved significant performance improvement on semantic role labeling (from 71.31% to 71.89%), especially in the out-domain evaluation (from 60.16% to 62.11%).
 
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