Chinese Event Detection Method Combining Attention Mechanism and BiLSTM

• 1、School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044

Abstract：Event detection is one of the important tasks in the field of information extraction. Most of the existing methods are highly dependent on complicated natural language feature engineering and processing tools. Chinese event detection even suffers from the problem that some trigger words are segmented by word segmentation. In this paper, Chinese event detection is regarded as a sequence labeling rather than a classification problem. A Chinese event detection model ATT-BiLSTM is proposed, which integrates attention mechanism and long short-term memory neural network. The attention mechanism is used to better capture global features and BiLSTM layers are employed to capture sequence features more effectively, which can improve the performance of Chinese event detection. Experiments on the ACE 2005 Chinese dataset show that the performance of the proposed method significantly outperforms other existing Chinese event detection methods.

Keywords： Chinese event detection attention mechanism long short-term memory