ZHOU Chuanhua, ZHOU Jiayi, YU Cai, ZHAO Wei, PAN Ruilin. Multi-channel Sliced Deep RCNN with Residual Network for Text Classification[J]. Chinese Journal of Electronics, 2020, 29(5): 880-886. doi: 10.1049/cje.2020.08.003
Citation: ZHOU Chuanhua, ZHOU Jiayi, YU Cai, ZHAO Wei, PAN Ruilin. Multi-channel Sliced Deep RCNN with Residual Network for Text Classification[J]. Chinese Journal of Electronics, 2020, 29(5): 880-886. doi: 10.1049/cje.2020.08.003

Multi-channel Sliced Deep RCNN with Residual Network for Text Classification

doi: 10.1049/cje.2020.08.003
Funds:  This work is supported by the National Natural Science Foundation of China (No.71772002, No.61702006) and The Open Fund of Key Laboratory of Anhui Higher Education Institutes (No.CS2020-04).
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  • Corresponding author: ZHOU Jiayi (corresponding author) was born in 1994. He received his B.S. degree in Computer Science and Technology from Southeast University. He received his M.S. degree in Information, Production and Systems from Waseda University. His research interests include intelligent algorithm, information visualization. (E-mail:421381551@qq.com)
  • Received Date: 2017-08-10
  • Rev Recd Date: 2019-12-21
  • Publish Date: 2020-09-10
  • We propose a multi-channel sliced deep Recurrent convolutional neural network (RCNN) with a residual network. We expand the RCNN into a deep neural network. Our proposed model can directly learn to extract bigram features and other features from sentences where other machine learning methods cannot. The experimental results indicate that our model outperforms the traditional methods.
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