中文摘要：

现有的中文事件触发词抽取方法大多数采用特征工程和触发词扩展方法,无法利用同一文档中各个触发词实例之间的内在关系。为了解决上述问题,基于马尔科夫逻辑网络（MLN）,利用核心词素,训练语料中触发词实例填充真假事件的概率,以及触发词实例间的关系等信息来推导测试集中缺乏有效上下文信息和低可信度的触发词实例。在ACE 2005中文语料上的实验结果表明,与基准系统相比,该方法在触发词识别和事件类型分类阶段F1值分别提高3.65%和2.51%。

英文摘要：

Previous Chinese argument extraction approaches mainly focus on feature engineering and trigger expansion, which cannot exploit inner relation between trigger mentions in same document. To address this issue, the authors bring forward a novel trigger inference mechanism based on Markov logic network. Head morpheme, the probabilities of a trigger mention fulfilling true and pseudo events from the training set and the relationships between trigger mentions are used to infer those trigger mentions with lack of effective context information or low confidences in testing set. Experimental results on the ACE 2005 Chinese corpus show that the proposed approach outperforms the baseline, with the F1 improvements of 3.65% and 2.51% in trigger identification and event type classification respectively.