中文摘要：

近年来,越来越多的研究关注事件时序关系,但大多数工作集中于提高事件关系分类器的性能,忽略了分类器错误所造成的事件关系间不一致的问题。该文利用了一个全局优化的推理模型来解决这一问题,将事件时序关系全局优化看成整数线性规划问题,使用了自反性、传递性、同指性、时序连接词、事件类型对等多个约束条件。实验结果表明,该文的全局推理方法与分类器相比,F1值提高了3.56%。

英文摘要：

In recently years, more and more studies are devoted to temporal relations between events, with a focuse on improving pairwise classifiers, ignoring the obvious inconsistent problems in the global space of events when mis classifications occur. In this paper, we use a global inference model to resolve such problem bytreating temporal relations recognition as Integer Linear Program. We use many constraints, such as reflexivity, transitivity, event coreference, temporal conjunctions, pairs of event types, etc. The experimental results show that the global inference model outperformed the local classifiers by 3.56% in F1.