Chinese Emergency Event Recognition Using Conv-RDBiGRU Model
Joint Authors
Wang, Guodong
Yin, Haoran
Cao, Jinxuan
Cao, Luzhe
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-21
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
In view of the weak generalization of traditional event recognition methods, the limitation of dependence on field knowledge of expert, the longer train time of deep neural network, and the problem of gradient dispersion, the neural network joint model, Conv-RDBiGRU, integrated residual structure was proposed.
Firstly, text corpus is preprocessed by word segmentation and stop words processing and uses word embedding to form the matrix of word vectors.
Then, local semantic features are extracted through convolution operation, and deep context semantic features are extracted through RDBiGRU.
Finally, the learned features are activated by softmax function and the recognition results are output.
The novelty of work is that we integrate residual structure into recurrent neural network and combine these methods and field of application.
The simulation results show that this method improves precision and recall of Chinese emergency event recognition, and the F-value is better than other methods.
American Psychological Association (APA)
Yin, Haoran& Cao, Jinxuan& Cao, Luzhe& Wang, Guodong. 2020. Chinese Emergency Event Recognition Using Conv-RDBiGRU Model. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138800
Modern Language Association (MLA)
Yin, Haoran…[et al.]. Chinese Emergency Event Recognition Using Conv-RDBiGRU Model. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1138800
American Medical Association (AMA)
Yin, Haoran& Cao, Jinxuan& Cao, Luzhe& Wang, Guodong. Chinese Emergency Event Recognition Using Conv-RDBiGRU Model. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138800
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138800