Character-Based LSTM-CRF with Semantic Features for Chinese Event Element Recognition.

Event element recognition is a significant task in event-based information extraction. In this paper, we propose an event element recognition model based on character-level embedding with semantic features. By extracting character-level features, the proposed model can capture more information of words. Our results show that joint character Convolutional Neural Networks (CNN) and character Bi-directional Long Short-Term Memory Networks (Bi-LSTM) is superior to single character-level model. In addition, adding semantic features such as POS (part-of-speech) and DP (dependency parsing) tends to improve the effect of recognition. We evaluated different methods in CEC (Chinese Emergency Corpus), and the experimental results show that our model can achieve good performance, and the F value of element recognition was 77.17%.