Abstract:

The recurrent neural network is widely used in the event extraction of Chinese text to extract events and event elements, but it usually loses essential information when processing long words. In this study, the convolutional neural network (CNN) and the bidirectional long short-term memory (Bi-LSTM) network were combined to develop a novel event extraction model known as CNN-Bi-LSTM-conditional random field (CRF). A joint vector of characters and words was adopted based on the attention mechanism and semantic features, and the CNN and Bi-LSTM models were used to process the vector to obtain its implicit representation. Finally, the CRF was used to obtain the prediction results. The experimental results show that the proposed method is more accurate than other existing event extraction methods in extracting Chinese text.

Key words: event extraction, convolutional neural network, long short-term memory