基于阅读理解框架的中文事件论元抽取

传统的事件论元抽取方法把任务当作句子中实体提及的多分类或序列标注任务，论元角色的类别在这些方法中只能作为向量表示，而忽略了论元角色的先验信息。实际上，论元角色的语义和论元本身有很大关系。对此，该文提议将其当作机器阅读理解任务，把论元角色转换为自然语言描述的问题，通过在上下文中回答这些问题来抽取论元。该方法更好地利用了论元角色类别的先验信息，在ACE2005中文语料上的实验证明了该方法的有效性。

Chinese Event Argument Extraction via Reading Comprehension Framework

Event argument extraction methods is usually formulated as a multi-classification or sequence labeling task to identify the mention by entities in the sentence. The category of argument roles are represented by vectors without considering their prior information. In fact, the semantics of argument role category is closely related with the argument itself. Therefore, this paper proposes to regard argument extraction as machine reading comprehension, with argument role described as natural language question. and the way to extract arguments is to answer these questions based on the context. This method can make better use of the prior information existed in argument role categories and its effectiveness is shown in the experiments of Chinese corpus of ACE 2005.