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Regional Bullying Text Recognition Based on Two-Branch Parallel Neural Networks

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Abstract

Traditional features and pipelined algorithms ignore the subspace semantic information and different information complementarity of regional bullying text when describing and recognizing regional bullying text. In order to solve the above problems, combined with features of Chinese, a regional bullying text recognition algorithm called Two-Branch Parallel Neural Network (TB-PNN) is proposed. First, the word vector, sentence vector, pinyin and tone features extracted by the word embedding technique and the character feature extracted by the Character Graph Convolutional Neural (CGCN). Secondly, TB-PNN is constructed by Multi-Head Self-Attention Mechanism (MHSA), Capsule Network (CapsNet) and Independent Recurrent Neural Network (IndRNN). The left branch was MHSA-CapsNet and the right branch was Multi-MHSA-IndRNN. The algorithm assigns weights to the fused features through MHSA, uses the CapsNet of the left branch to mine the key features with high weight and generates vector tags, and uses the IndRNN of the right branch to capture the subspace semantic information of the key features in the text. The left and right branches form complementary information. Finally, SoftMax classifier is used to realize the accurate recognition of regional bullying text. The experimental results show that TB-PNN algorithm can effectively improve the recognition accuracy of regional bullying text.

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ACKNOWLEDGMENTS

We also thank my tutor for his careful guidance and all participants for their insightful comments.

Funding

The research is partially supported by the National Natural Science Foundation of China (nos. 61563051, 61662074, and 61262064), the Key Project of the National Natural Science Foundation of China (no. 61331011), the Xinjiang Uygur Autonomous Region Scientific and Technological Personnel Training Project (no. QN2016YX0051), the Xinjiang Tianshan Youth Project (no. 2017Q011), and the Research and Innovation Project of Postgraduate in Autonomous Region (no. XJ2019G070).

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Authors and Affiliations

  1. School of Software, Key Laboratory of Software Engineering Technology, Xinjiang University, 830008, Urumqi, Xinjiang, China

    Zhao Meng & Shengwei Tian

  2. Network Center, Xinjiang University, 830046, Urumqi, Xinjiang, China

    Long Yu

Authors
  1. Zhao Meng
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  2. Shengwei Tian
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  3. Long Yu
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Corresponding author

Correspondence to Shengwei Tian.

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The authors declare that they have no conflicts of interest.

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Zhao Meng, Tian, S. & Yu, L. Regional Bullying Text Recognition Based on Two-Branch Parallel Neural Networks. Aut. Control Comp. Sci. 54, 323–334 (2020). https://doi.org/10.3103/S0146411620040082

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