Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase. We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. To the best our knowledge, this is one of the first efforts to handle multi-word events and also the first attempt to use RNNs for event detection. Experimental results demonstrate that FBRNN is competitive with the state-of-the-art methods on the ACE 2005 and the Rich ERE 2015 event detection tasks.
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机译：传统的事件检测方法在很大程度上依赖于人工设计的丰富功能。最近的深度学习方法通过自动特征工程减轻了这个问题。但是，到目前为止，此类努力（就像传统方法一样）仅集中于单个令牌事件的提及，而实际上，事件也可以是一个短语。相反，我们使用前后反向递归神经网络（FBRNN）来检测可能是单词或短语的事件。据我们所知，这是处理多单词事件的第一个尝试，也是使用RNN进行事件检测的第一个尝试。实验结果表明，FBRNN与ACE 2005和Rich ERE 2015事件检测任务上的最新方法相比具有竞争力。
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