Event Recognition Based on Deep Belief Network

Event recognition is critical to information extraction. To overcome limitations of the exiting event recognition approaches, we proposed an event recognition model based on deep learning (DL-ERM). Firstly, we acquired candidate words through a word segmentation system and classified them into five categories. Then, we selected six recognition feature layers and constructed corresponding feature representation rules to convert words into vector samples. Finally, we employed a deep belief network (DBN) to extract deep semantic features of words, and used a back propagation neural network to identify events. The results of experiments show that the maximum F-measure is 85.17%. Furthermore, we presented a hybrid-supervised DBN, which combines the unsupervised and supervised learning. The novel DBN improves the recognition performance (89.2% F-measure) and effectively controls the training time (increased by 27.50%). © 2017, Chinese Institute of Electronics. All right reserved.