JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (5): 36-45.doi: 10.6040/j.issn.1671-9352.0.2021.790

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Text classification model based on dual-channel feature fusion based on XLNet

MENG Jinxu1, SHAN Hongtao1*, HUANG Runcai1, YAN Fengting3, LI Zhiwei1, ZHENG Guangyuan2, LIU Yiming1, SHI Changtong1   

  1. 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;
    2. College of Information Technologhy, Shanghai Jianqiao University, Shanghai 201306, China;
    3. Shanghai Qingxi Intelligent Technology Co., Ltd., Shanghai 201800, China
  • Published:2023-05-15

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Abstract: A two-channel feature-fusion text classification(XLNet-CNN-BiGRU, XLCBG)model based on XLNet is proposed. Compared with the single model channel, the XLCBG model can extract richer semantic features by integrating the feature information of XLNet+CNN and XLNet+BiGRU channels to diversify the methods of feature-fusion processing and increase the selectivity of the optimal classification model. The XLCBG model adopts Maxpooling, Avgpooling, and attention mechanism to extract the vector with the largest feature value in the global, the feature vector with the mean value in the global, and the key features of attention mechanism to replace the whole vector, respectively. Finally, the current popular text classification models are compared with the XLCBG model. The experimental results show that the XLCBG-S model exhibits better classification performance than other models on the Chinese THUCNews dataset. At the same time, the XLCBG-Ap model exhibits better classification performance than other models on the English AG News data set. In the English 20NewsGroups data set, the XLCBG-Att model is superior to other models in accuracy and recall rate, and the XLCBG-Mp model is superior to other models in accuracy rate and F1.

Key words: XLNet, dual-channel, text classification, BiGRU, CNN

CLC Number: 

  • TP391.1
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