Descending-Path Convolution Kernel for Syntactic Structures
Chen Lin, Timothy A. Miller, Alvin T. Kho, Steven J. Bethard, Dmitriy Dligach, Sameer Pradhan, Guergana Savova · 2014
Convolution tree kernels are an efficient and effective method for comparing syntac-tic structures in NLP methods. However, current kernel methods such as subset tree kernel and partial tree kernel understate the similarity of very similar tree structures. Although soft-matching approaches can im-prove the similarity scores, they are corpus-dependent and match relaxations may be task-specific. We propose an alternative ap-proach called descending path kernel which gives intuitive similarity scores on compa-rable structures. This method is evaluated on two temporal relation extraction tasks and demonstrates its advantage over rich syntactic representations. 1