A Neural Network Based Text Classification with Attention Mechanism

被引:0
|
作者
Lu SiChen [1 ]
机构
[1] Jilin Normal Univ, BoDa Coll, Siping, Peoples R China
来源
PROCEEDINGS OF 2019 IEEE 7TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2019) | 2019年
关键词
attention mechanism; convolutional neural network; text classification; natural language processing; artificial intelligence;
D O I
10.1109/iccsnt47585.2019.8962513
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Text classification is a basic task in Natural Language Processing field. As neural networks gains great breakthroughs in computer vision and speech recognition, neural networks based model, such as convolutional neural networks and recurrent neural networks, is also proved to be powerful in many Natural Language Processing tasks compared to traditional approaches. Based on recurrent neural networks with attention mechanism, a new model incorporated enhanced text representation by means of convolutional neural networks is proposed to deal with text classification task. The experimental results show that the proposed model gains higher accuracy compared to the common attention based recurrent neural networks model.
引用
收藏
页码:333 / 338
页数:6
相关论文
共 50 条
  • [1] Text Classification Based on Graph Convolution Neural Network and Attention Mechanism
    Zhai, Sheping
    Zhang, Wenqing
    Cheng, Dabao
    Bai, Xiaoxia
    ACM International Conference Proceeding Series, 2022, : 137 - 142
  • [2] Text Classification Based on Convolutional Neural Network and Attention Model
    Yang, Shuang
    Tang, Yan
    2020 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD 2020), 2020, : 67 - 73
  • [3] Chinese text classification based on attention mechanism and feature-enhanced fusion neural network
    Jinbao Xie
    Yongjin Hou
    Yujing Wang
    Qingyan Wang
    Baiwei Li
    Shiwei Lv
    Yury I. Vorotnitsky
    Computing, 2020, 102 : 683 - 700
  • [4] Chinese text classification based on attention mechanism and feature-enhanced fusion neural network
    Xie, Jinbao
    Hou, Yongjin
    Wang, Yujing
    Wang, Qingyan
    Li, Baiwei
    Lv, Shiwei
    Vorotnitsky, Yury, I
    COMPUTING, 2020, 102 (03) : 683 - 700
  • [5] An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism
    Li, Wenkuan
    Liu, Peiyu
    Zhang, Qiuyue
    Liu, Wenfeng
    FUTURE INTERNET, 2019, 11 (04):
  • [6] Text Classification with Attention Gated Graph Neural Network
    Zhaoyang Deng
    Chenxiang Sun
    Guoqiang Zhong
    Yuxu Mao
    Cognitive Computation, 2022, 14 : 1464 - 1473
  • [7] Text Classification with Attention Gated Graph Neural Network
    Deng, Zhaoyang
    Sun, Chenxiang
    Zhong, Guoqiang
    Mao, Yuxu
    COGNITIVE COMPUTATION, 2022, 14 (04) : 1464 - 1473
  • [8] Knowledge attention sandwich neural network for text classification
    Zhan, Zhiqiang
    Hou, Zifeng
    Yang, Qichuan
    Zhao, Jianyu
    Zhang, Yang
    Hu, Changjian
    NEUROCOMPUTING, 2020, 406 : 1 - 11
  • [9] Fault text data classification based on mutual attention mechanism network model
    Liu P.
    Sun L.
    Zhang C.
    Wang B.
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2021, 27 (01): : 72 - 89
  • [10] Commented Content Classification with Deep Neural Network Based on Attention Mechanism
    Zhao, Qinlu
    Cai, Xiaodong
    Chen, Chaocun
    Lv, Lu
    Chen, Mingyao
    2017 IEEE 2ND ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC), 2017, : 2016 - 2019