电子测量技术 Electronic Measurement Technology ISSN 1002-7300,CN 11-2175/TN 行优化，其隐藏层由两个残差块组成，并引入注意力机制进一步改善预测的准确度。在多个公开数据 集中进行二元分类和多元主题分类实验。实验结果表明，与其他优秀方法相比，所提方法在准确率、召回率和 F1 得分方面 的性能更优，最高准确度达 %，最高 F1 得分为 %。 关键词：文本分类；GloVe 模型；一维卷积神经网络；双向长短期记忆网络；注意力 中图分类号： 文献标识码：A 国家标准学科分类代码： Text classification method based on GloVe model and attention mechanism Bi-LSTM ZHOU Yan (College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China)
Abstract：To improve the accuracy of text classification and expand different classification tasks, a text classification method combining one-dimensional convolutional neural network (1D-CNN) and bi-directional long short-term memory (Bi-LSTM) network is proposed. Firstly, in order to solve the difficulty of representing synonyms and polysemy, GloVe model is used to represent word features, making full use of the advantages of global information and co-occurrence wi
