title: Event Recognition Based on Deep Learning in Chinese Texts.
abstract: Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.
authors: Shanghai University, School of Computer Engineering and Science, Shanghai, China.
authors: Yajun
authors: Y
authors: Zhang
authors: Shanghai University, School of Computer Engineering and Science, Shanghai, China.
authors: Zongtian
authors: Z
authors: Liu
authors: Shanghai University, School of Computer Engineering and Science, Shanghai, China.
authors: Wen
authors: W
authors: Zhou
mesh_terms: D000465:Algorithms; D002681:China; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual; D012660:Semantics