基于图模型的语义角色标注重排序

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。

Graphical Model Based Semantic Role Labeling Reranking

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 2.4 points in term of F score, and obtain the best results on this data set without using any system combine techniques.
Key wordssemantic role labeling; graphical model; reranking