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A joint syntactic and semantic dependency parsing system based on maximum entropy models

Buzhou Tang, Lu Li, Xinxin Li, Xuan Wang, Xiaolong Wang
2009 Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task - CoNLL '09   unpublished

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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,
more » ... 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.
doi:10.3115/1596409.1596428 fatcat:mlxlwopiuffdnmmwhtak3f6uay
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Buzhou Tang, Lu Li, Xinxin Li, Xuan Wang, Xiaolong Wang. "A joint syntactic and semantic dependency parsing system based on maximum entropy models." Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task - CoNLL '09 (2009)