Information extraction of the history evolution based on hybrid convolution tree kernel
Changbo Tian, Min Lin, Siriguleng · 2015
A hybrid convolution tree kernel is applied to extract of the history evolution information. The hybrid kernel consists of two individual convolution kernels: a Path kernel, which captures predicate-argument link features, and a Constituent Structure kernel, which captures the syntactic structure features of arguments. The Predicate-Arguments Feature (PAF) kernel was extracted and decomposed into Constituent Structure kernel and Path kernel. The linear combination of Constituent Structure kernel and Path kernel improve the accuracy and efficiency in this task. The experimental results show this method performs better.