Paper: Descending-Path Convolution Kernel for Syntactic Structures

ACL ID P14-2014
Title Descending-Path Convolution Kernel for Syntactic Structures
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 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.