This paper tackles the task of event detection, which involves identifying and categorizing events. The previous work mainly exists two problems: (1) the traditional feature-based methods apply crosssentence information, yet need taking a large amount of human effort to design complicated feature sets and inference rules; (2) the representation-based methods though overcome the problem of manually extracting features, while just depend on local sentence representation. Considering local

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... context is insufficient to resolve ambiguities in identifying particular event types, therefore, we propose a novel document level Recurrent Neural Networks (DLRNN) model, which can automatically extract cross-sentence clues to improve sentence level event detection without designing complex reasoning rules. Experiment results show that our approach outperforms other state-ofthe-art methods on ACE 2005 dataset neither the external knowledge base nor the event arguments are used explicitly.