In this paper, a novel kernel-based method is presented for the problem of relation extraction between named entities from Chinese texts. The kernel is defined over the original Chinese string representations around particular entities. As a kernel function, the Improved-Edit-Distance (IED) is used to calculate the similarity between two Chinese strings. By employing the Voted Perceptron and Support Vector Machine (SVM) kernel machines with the IED kernel as the classifiers, we tested the method by extracting person-affiliation relation from Chinese texts. By comparing with traditional feature-based learning methods, we conclude that our method needs less manual efforts in feature transformation and achieves a better performance.
REF defined an improved edit distance kernel over the original Chinese string representation around particular entities.
Improved-Edit-Distance Kernel for Chinese Relation Extraction
Second International Joint Conference on Natural Language Processing: Companion Volume including Posters/Demos and tutorial abstracts
Computer Science