基于图模型的语义角色标注重排序

Graphical Model Based Semantic Role Labeling Reranking

传统的语义角色标注模型使用的都是本地特征,不利于捕捉一些全局性的标注错误。该文提出使用图模型对语义角色标注结果进行重排序,利用标记传播迭代算法对标注的结果进行重排序,保证全局标注结果的一致性。该文在PropBank上的实验表明,采用重排序后标注性能有了2.4个F值的显著提升。在不使用系统融合技术的情况下,标注的结果是当前世界最好的性能。

Traditional methods for Semantic Role Labeling（SRL）generally utilize some local features to identify and classify the semantic roles which are hard to capture labeling inconsistency.In this paper,we propose a graphical model to rerank the results via label propagation algorithm.Experimental results on PropBank show that our models significantly improve the performance 2.4points in term of F score,and obtain the best results on this data set without using any system combine techniques.