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A hybrid re-fusion model for text classification.

Qi Liu1, Kejing Xiao2, Zhaopeng Qian3

  • 1School of Information Engineering, Beijing Institute of Graphic Communication, Beijing, China.

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Summary
This summary is machine-generated.

The new XLG-Net model enhances text classification by integrating XLNet and GCNII, improving long-distance dependency capture and addressing over-smoothing for better accuracy on complex tasks.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Deep Learning

Background:

  • Text classification is crucial for organizing text data.
  • Existing models like BertGCN have limitations in handling long sequences and deep networks.
  • BERT struggles with long-distance dependencies, while GCN faces over-smoothing issues.

Purpose of the Study:

  • To propose the XLG-Net model for enhanced text classification performance.
  • To overcome limitations of BERT and GCN in complex text classification tasks.
  • To improve accuracy and robustness for both long and short texts.

Main Methods:

  • Integrating XLNet for improved long-distance dependency capture and complex structure understanding.
  • Utilizing GCNII to mitigate the over-smoothing problem in Graph Convolutional Networks.
  • Applying the DoubleMix approach to XLNet for hybrid hidden state mixing.

Main Results:

  • XLG-Net demonstrates significant performance improvements on four benchmark text classification datasets.
  • The model effectively handles long-distance dependencies and complex language structures.
  • Improved accuracy and robustness were observed for both long and short text classification.

Conclusions:

  • XLG-Net offers a superior approach to complex text classification tasks.
  • The integration of XLNet and GCNII effectively addresses previous model limitations.
  • XLG-Net shows strong potential for advancing natural language processing applications.