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

摘要

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.