基于混合神经网络的文本分类方法

为更好解决卷积神经网络提取特征不充分,难以处理长文本结构信息和捕获句子语义关系等问题,提出一种融合CNN和自注意力BiLSTM的并行神经网络模型TC-ABlstm.对传统的卷积神经网络进行改进,增强对文本局部特征的提取能力;设计结合注意力机制的双向长短期记忆神经网络模型来捕获文本上下文相关的全局特征;结合两个模型提取文...

Text classification method based on hybrid neural network

To better solve the problems of insufficient feature extraction and difficulty in processing long text structure information and capturing sentence semantic relations,a parallel neural network model TC-ABlstm combining CNN and self-attention BiLSTM was proposed.The traditional convolution neural network was improved to enhance the ability of extracting local features of text.A bidirectional long-term and short-term memory neural network model combined with attention mechanism was designed to capture the global features of text context.The advantages of the two models were combined to extract text features to improve the accuracy of classification.Results of experiments on Sogou corpus and Fudan University Chinese corpus show that the proposed model can effectively improve the accuracy of text classification.