Event Extraction via Extracting Triggers and Arguments Simultaneously and Matching

In recent years, researchers have proposed various methods to achieve better results on the event extraction task. Among them, the pipelined-based methods first perform event trigger prediction and then identify arguments in separate stages. Therefore, the errors from the trigger identification propagate the argument identification. The joint-based methods generally consider the argument role task as a multi-class classification problem, which undoubtedly reduces efficiency. To address these two shortcomings, in this paper, we propose an effective neural model for event extraction. Specifically, we use a Named Entity Recognition method to label arguments while extracting event triggers. As a result, the labels contain arguments and their role information. If the arguments are assigned to event triggers, we assume that there are inter-dependencies between them. Inspired by their inter-dependencies, we then assign arguments to event triggers by matching. This matching operation greatly improves efficiency as its essence is the binary classification. The experimental results on Chinese Emergency Corpus show that our model is more effective and competitive than baselines.