Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220700039-5.doi: 10.11896/jsjkx.220700039

• Artificial Intelligence • Previous Articles     Next Articles

Text Classification Based on Weakened Graph Convolutional Networks

HUANG Yujiao1, CHEN Mingkai1, ZHENG Yuan1, FAN Xinggang1, XIAO Jie2, LONG Haixia2   

  1. 1 Zhijiang College of Zhejiang University of Technology,Shaoxing,Zhejiang 312030,China;
    2 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310000,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:HUANG Yujiao,born in 1985,Ph.D,associate professor.Her main research interests include deep learning,text data analysis and dynamic characteristics of neural networks.
  • Supported by:
    National Natural Science Foundation of China(61972354,62106225) and Natural Science Foundation of Zhejiang Pvovince,China(LY20F020024,LZ22F020011).

PDF (PC)

240

Abstract: Text classification is a classic problem in the field of natural language processing.The traditional text classification model needs to extract features manually,the classification accuracy is not high,and it is difficult to deal with non-European spatial data.In order to solve the above problems and further improve the accuracy of text classification,the W-GCN model is proposed.This model is improved on the basis of the Text-GCN model,and a new weakened structure model is established to replace the text-GCN model.The dropout operation of neurons,and by weakening the weight,accurately control the weakening strength,and on the basis of retaining the dropout to a certain extent to prevent overfitting,it avoids the loss of features caused by directly discarding neurons,thus improving the accuracy of model classification..Compared with the Text-GCN model,the W-GCN model based on the weakened graph convolutional network improves the accuracy by 0.38% on the R8 dataset and 0.62% on the R52 dataset.The experimental results prove that the model Improve and weaken the effectiveness of the structure.

Key words: Graph convolutional neural networks, Text classification, Construction method of text map, Weakened structure, Droupout

CLC Number: 

  • TP391
[1]CARRERAS X,MARQUEZ L.Boosting trees for anti-spamemail filtering[C]//Proceedings of European Conference Recent Advances in NLP.2001:58-64.
[2]AGGARWAL C C,ZHAI C X.A survey of text classification algorithms[M].US:Mining Text Data,Springer,2012.
[3]MCCALLUM A,NIGAM K.A comparison of event models for naive bayes text classificatin[C]//AAAI-98 Workshop on Learning for Text Categorization.1998:41-48.
[4]JOACHIMS T.Text categorization with support vector ma-chines:Learning with many relevant features[C]//Proceedings of the 10th European Conference on Machine Learning.1998:137-142.
[5]WANG X,CHEN R,JIA Y,et al.Short text classification using wikipedia concept based document representation[C]//International Conference on Information Technology and Applications.2013:471-474.
[6]LECUN Y,BOTTOU L.Gradient-based learning applied to docu-ment recognition[C]//Proceedings of the IEEE.1998:2278-2324.
[7]ZHOU J,CUI G,ZHANG Z,et al.Graph neural networks:A review of methods and applications[J].arXiv:1812.08434,2018.
[8]WU Z,PAN S,CHEN F,et al.A Comprehensive Survey onGraph Neural Networks[J].IEEE Transactions on Neural Networks and Learning Systems,2021,32(1):4-24.
[9]ZHANG Z,CUI P,ZHU W. Deep Learning on Graphs:A Survey[J].IEEE Transactions on Knowledge and Data Engineering,2022,34(1):249-270.
[10]ZHOU J,CUI G,HU S,et al.Graph neural networks:A review of methods and applications[J].AI Open,2020,1:57-81.
[11]DEFFERRARD M,BRESSON X,VANDERGHEYNST P,et al.Convolutional neural networks on graphs with fastlocali-zed spectral filtering[C]//Neural Information Processing Systems.2016:3844-3852.
[12]YAO L,MAO C,LUO Y.Graph Convolutional Networks for Text Classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:7370-7377.
[13]HINTON G E,SRIVASTAVA N,KRIZHEVSKY A,et al.Improving neural networks by preventing co-adaptation of feature detectors[J].Computer Science,2012,3(4):212-223.
[14]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[15]BLEID M,NG A Y,JORDAN M I.Latent dirichlet allocation[J].Journal of Machine Learning Research,2003,3(1):933-1022.
[16]LAI S,XU L,LIU K,et al.Recurrent convolutional neural networks for text classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2015:2267-2273.
[17]CHEN B.Graph convolution neural network algorithm for text classification[D].Guilin:Guangxi Normal University,2021.
[18]WANG T,ZHU X F,TANG G.Text classification based on graph convolution neural network with knowledge enhancement[J].Journal of Zhejiang University (Engineering Edition),2022,56(2):322-328.
[1] LIANG Haowei, WANG Shi, CAO Cungen. Study on Short Text Classification with Imperfect Labels [J]. Computer Science, 2023, 50(1): 185-193.
[2] ZHENG Cheng, MEI Liang, ZHAO Yiyan, ZHANG Suhang. Text Classification Method Based on Bidirectional Attention and Gated Graph Convolutional Networks [J]. Computer Science, 2023, 50(1): 221-228.
[3] WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[4] TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216.
[5] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[6] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[7] DENG Kai, YANG Pin, LI Yi-zhou, YANG Xing, ZENG Fan-rui, ZHANG Zhen-yu. Fast and Transmissible Domain Knowledge Graph Construction Method [J]. Computer Science, 2022, 49(6A): 100-108.
[8] KANG Yan, WU Zhi-wei, KOU Yong-qi, ZHANG Lan, XIE Si-yu, LI Hao. Deep Integrated Learning Software Requirement Classification Fusing Bert and Graph Convolution [J]. Computer Science, 2022, 49(6A): 150-158.
[9] SHAO Xin-xin. TI-FastText Automatic Goods Classification Algorithm [J]. Computer Science, 2022, 49(6A): 206-210.
[10] DENG Zhao-yang, ZHONG Guo-qiang, WANG Dong. Text Classification Based on Attention Gated Graph Neural Network [J]. Computer Science, 2022, 49(6): 326-334.
[11] LIU Shuo, WANG Geng-run, PENG Jian-hua, LI Ke. Chinese Short Text Classification Algorithm Based on Hybrid Features of Characters and Words [J]. Computer Science, 2022, 49(4): 282-287.
[12] ZHONG Gui-feng, PANG Xiong-wen, SUI Dong. Text Classification Method Based on Word2Vec and AlexNet-2 with Improved AttentionMechanism [J]. Computer Science, 2022, 49(4): 288-293.
[13] DENG Wei-bin, ZHU Kun, LI Yun-bo, HU Feng. FMNN:Text Classification Model Fused with Multiple Neural Networks [J]. Computer Science, 2022, 49(3): 281-287.
[14] ZHANG Hu, BAI Ping. Graph Convolutional Networks with Long-distance Words Dependency in Sentences for Short Text Classification [J]. Computer Science, 2022, 49(2): 279-284.
[15] YANG Xu-hua, JIN Xin, TAO Jin, MAO Jian-fei. Text Classification Based on Graph Neural Networks and Dependency Parsing [J]. Computer Science, 2022, 49(12): 293-300.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.