Computer Science ›› 2018, Vol. 45 ›› Issue (6): 235-240.doi: 10.11896/j.issn.1002-137X.2018.06.042

• Artificial Intelligence • Previous Articles     Next Articles

Convolutional Neural Network Model for Text Classification Based on BGRU Pooling

ZHOU Feng, LI Rong-yu   

  1. School of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China
  • Received:2017-05-03 Online:2018-06-15 Published:2018-07-24

Abstract: Aiming at the problem thatdeep learning has the disadvantages of small adaptability and low precision when it solves the problem of text classification,this paper proposed a convolution neural network model based on bi-directional gated recurrent unit (BGRU) and convolution layer pooling.In the pooling stage,the intermediate sentence gene-rated by BGRU is represented as a local representation obtained from the convolution layer,the representation of high similarity is judged to be important information,and the information is retained by increasing its weight.The model can give end-to-end training and train multiple types of text,and it has good adaptability.The experimental results show that the proposed model has greate advantage compared with other similar models,and the classification accuracy is also improved significantly.

Key words: Bi-directional gated recurrent unit, Convolutional neural network, Deep learning, Text classification

CLC Number: 

  • TP183
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