On building a more efficient grammar by exploiting types
Natural Language Engineering
Semantic role labeling using dependency trees
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Partial parse selection for robust deep processing
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Hybrid multilingual parsing with HPSG for SRL
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Cross-domain dependency parsing using a deep linguistic grammar
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Evaluating contribution of deep syntactic information to shallow semantic analysis
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Recognizing textual relatedness with predicate-argument structures
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Convolution kernels for opinion holder extraction
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
MARS: A specialized RTE system for parser evaluation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
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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%).