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.

中文翻译：

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基于两分支并行神经网络的区域欺凌文本识别

摘要

传统特征和流水线算法在描述和识别区域欺凌文本时会忽略子空间语义信息以及区域欺凌文本的不同信息互补性。为了解决上述问题，结合中文的特点，提出了一种区域性欺凌文本识别算法，称为两支并行神经网络（TB-PNN）。首先，通过词嵌入技术提取的词向量，句子向量，拼音和音调特征以及通过字符图卷积神经网络（CGCN）提取的字符特征。其次，TB-PNN由多头自我注意机制（MHSA），胶囊网络（CapsNet）和独立循环神经网络（IndRNN）构成。左分支是MHSA-CapsNet，右分支是Multi-MHSA-IndRNN。该算法通过MHSA为融合特征分配权重，使用左分支的CapsNet挖掘具有高权重的关键特征并生成矢量标记，并使用右分支的IndRNN捕获关键特征的子空间语义信息。文本。左右分支形成补充信息。最后，使用SoftMax分类器实现对区域欺凌文本的准确识别。实验结果表明，TB-PNN算法可以有效提高区域欺凌文本的识别精度。左右分支形成补充信息。最后，使用SoftMax分类器实现对区域欺凌文本的准确识别。实验结果表明，TB-PNN算法可以有效提高区域欺凌文本的识别精度。左右分支形成补充信息。最后，使用SoftMax分类器实现对区域欺凌文本的准确识别。实验结果表明，TB-PNN算法可以有效提高区域欺凌文本的识别精度。