Study of Kernel-Based Methods for Chinese Relation Extraction

In this paper, we mainly explore the effectiveness of two kernelbased methods, the convolution tree kernel and the shortest path dependency kernel, for Chinese relation extraction based on ACE 2007 corpus. For the convolution kernel, the performances of different parse tree spans involved in it for relation extraction are studied. Then, experiments with composite kernels, which are a combination of the convolution kernel and feature-based kernels are presented in order to discuss the complementary effects between tree kernel and flat kernels. For the shortest path dependency kernel, we improve it by replacing the strict same length requirement with finding the longest common subsequences between two shortest dependency paths. Experiments show kernel-based methods are effective for Chinese relation extraction.