Tree Kernel-Based Semantic Role Classification in Chinese Language
Peifeng Li · Zhongwen xinxi xuebao · 2011
This paper explores semantic role classification in Chinese language via tree kernel methods,focusing on how to effectively capture the inherent structured knowledge in a parse tree.It extends the minimum syntactic structure and explores three syntactic structures with respect to the characteristics of semantic role classification.It also explores composite kernel to integrate feature-based methods and kernel-based methods.Evaluation on the Chinese PropBank shows that the tree kernel-based semantic role classification achieves a performance of 91.79% in accuracy.Moreover,our tree kernel method shows complementary to the feature-based methods and further boosts the performance to 94.28% in accuracy,better than the state-of-the-art.