A comprehensive study of tree kernels

Abstract

Tree kernels are an effective method to capture the structural information of tree data of various applications and many algorithms have been proposed. Nevertheless, we do not have sufficient knowledge about how to select good kernels. To answer this question, we focus on 32 tree kernel algorithms defined within a certain framework to engineer positive definite kernels, and investigate them under two different parameter settings. The result is amazing. Three of the 64 tree kernels outperform the others, and their superiority proves statistically significant through t-tests. These kernels include the benchmark tree kernels proposed in the literature, while many of them are introduced and tested for the first time in this paper.