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Pattern Recognition and Artificial Intelligence  2023, Vol. 36 Issue (7): 602-612    DOI: 10.16451/j.cnki.issn1003-6059.202307003
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Semi-Supervised Short Text Classification Based on Gated Double-Layer Heterogeneous Graph Attention Network
JIANG Yunliang1,2,3, WANG Qingpeng1,2, ZHANG Xiongtao1,2, HUANG Xu2,4, SHEN Qing1,2, RAO Jiafeng1,2
1. School of Information Engineering, Huzhou University, Huzhou 313000;
2. Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000;
3. School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004;
4. School of Science and Engineering, Huzhou College, Huzhou 313000

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Abstract  

To address the issues of insufficient utilization of information between nodes and overfitting in short text classification based on heterogeneous graph neural network, a method for semi-supervised short text classification based on gated double-layer heterogeneous graph attention network(GDHG) is proposed. GDHG consists of two layers: node attention and gated heterogeneous graph attention network. Firstly, different types of node attention coefficients are trained by node attention, and then the node attention coefficient is input into the gated heterogeneous graph attention network to obtain the gated double-layer attention. Secondly, the gated double-layer attention is multiplied by different states of the nodes to acquire the aggregated node features. Finally, the short texts are classified with the softmax function. In the proposed GDHG, the information forgetting mechanism of node attention and gated heterogeneous graph attention network is utilized to aggregate node information. Consequently, the information of neighboring nodes is effectively obtained. And then the hidden information of different neighboring nodes is mined to improve the ability to aggregate information from remote nodes. Experiment on four short text datasets , Twitter, MR, Snippets and AGNews, illustrate the superiority of GDHG.

Key wordsGated Heterogeneous Graph Attention      Semi-Supervised Learning      Heterogeneous Graph Neural Network      Short Text Classification     
Received: 06 December 2022     
ZTFLH: TP181  
  TP391  
Fund:

Regional Joint Fund for Innovation and Development of National Natural Science Foundation of China(No.U22A20102), “Pioneer” and “Leading Goose” Research and Deve-lopment Program of Zhejiang Province (No.2023C01150)

Corresponding Authors: ZHANG Xiongtao, Ph.D., associate professor. His research interests include artificial intelligence, pattern recognition and machine learning.   
About author:: JIANG Yunliang, Ph.D., professor. His research interests include intelligent information processing and geographic information system. WANG Qingpeng, master. His research interests include natural language processing and machine learning. HUANG Xu, Ph.D., associate professor. His research interests include artificial inte-lligence, pattern recognition and machine learning. SHEN Qing, master, professor. Her research interests include intelligent information processing. RAO Jiafeng, master. His research inte-rests include intelligent information processing and machine learning.
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JIANG Yunliang
WANG Qingpeng
ZHANG Xiongtao
HUANG Xu
SHEN Qing
RAO Jiafeng
Cite this article:   
JIANG Yunliang,WANG Qingpeng,ZHANG Xiongtao等. Semi-Supervised Short Text Classification Based on Gated Double-Layer Heterogeneous Graph Attention Network[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(7): 602-612.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202307003      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I7/602
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