Paper: Improved-Edit-Distance Kernel for Chinese Relation Extraction

ACL ID I05-2023
Title Improved-Edit-Distance Kernel for Chinese Relation Extraction
Venue International Joint Conference on Natural Language Processing
Session poster-demo-tutorial
Year 2005

In this paper, a novel kernel-based method is presented for the problem of relation extraction between named entities from Chinese texts. The ker- nel is defined over the original Chi- nese string representations around par- ticular entities. As a kernel func- tion, the Improved-Edit-Distance (IED) is used to calculate the similarity be- tween two Chinese strings. By em- ploying the Voted Perceptron and Sup- port Vector Machine (SVM) kernel ma- chines with the IED kernel as the clas- sifiers, we tested the method by extract- ing person-affiliation relation from Chi- nese texts. By comparing with tradi- tional feature-based learning methods, we conclude that our method needs less manual efforts in feature transformation and achieves a better performance.