Abstract The result of event argument recognition cannot guide event type recognition in the traditional multi-step event extraction methods. Nevertheless the performance of event extraction system largely depends on event type recognition. In order to address the backward dependency of event type recognition on event argument recognition, event extraction is considered as a sequence labeling. In this paper, an improved conditional random field joint labeling model is proposed. The event type and event argument are labelled simultaneously in the graph model. The solution of the unbalanced data problem is discussed through embedding trigger word. The experiments on ACE 2005 Chinese corpus show that the performance of event type recognition is improved by the proposed method and F-score achieves 63.53%.
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2012, Vol. 25 
