Event Detection with Forward-Backward Recurrent Neural Networks

Graduation date:2017Automatic event extraction from natural text is an important and challenging task for natural language understanding. 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 described with a phrase. In this thesis, we introduce and apply forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. 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