Research on Unsupervised Chinese Entity Relation Extraction Based on Convolution Tree Kernel
Qiaoming Zhu · Zhongwen xinxi xuebao · 2010
This paper proposes a convolution tree kernelbased approach for unsupervised Chinese entity relation extraction.This method first represents potential relation instances as shortest path-enclosed trees,then computes similarities between them using convolution tree kernel,finally groups them into various clusters through hierarchical clustering algorithms.Evaluation on the ACE RDC 2005 benchmark corpus shows that the convolution tree kernel-based approach achieves the highest F-measure of 60.1 on the task of unsupervised Chinese entity relation extraction,suggesting that this method is promising.