Discriminative learning and spanning tree algorithms for dependency parsing
Discriminative learning and spanning tree algorithms for dependency parsing
Adding semantic roles to the chinese treebank
Natural Language Engineering
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
Discriminative learning of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A cascaded syntactic and semantic dependency parsing system
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
The CoNLL-2009 shared task: syntactic and semantic dependencies in multiple languages
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
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A joint syntactic and semantic dependency parsing system submitted to the CoNLL-2009 shared task is presented in this paper. The system is composed of three components: a syntactic dependency parser, a predicate classifier and a semantic parser. The first-order MSTParser is used as our syntactic dependency pasrser. Projective and non-projective MSTParsers are compared with each other on seven languages. Predicate classification and semantic parsing are both recognized as classification problem, and the Maximum Entropy Models are used for them in our system. For semantic parsing and predicate classifying, we focus on finding optimized features on multiple languages. The average Macro F1 Score of our system is 73.97 for joint task in closed challenge.