Semantic Role Labeling in Chinese Language Based on Tree Kernel Function
Wang Bu-kang, Hongling Wang, Guodong Zhou · Jisuanji gongcheng · 2011
This paper implements a semantic role labeling in Chinese language,uses the convolution tree kernel of Support Vector Machine(SVM).It focuses on how to properly express the structural representation between predicates and arguments on dependency tree and let the input tree contain less noise information.It explores two methods to prune the dependency tree: Shortest Path Tree(SPT) and Minimum Tree(MT).Experimental results on the transferred corpuses from Chinese PropBank and Chinese NomBank show the system achieves 83.66 in labeled F1 on verbal predicates and 76.87 in labeled F1 on nominal predicates