计算机工程与应用2019，Vol.55Issue(24)：135-140,170,7.DOI:10.3778/j.issn.1002-8331.1809-0015
基于改进的CBOW与ABiGRU的文本分类研究
Text Classification Research Based on Improved CBOW and ABiGRU
摘要
Abstract
The representation and the feature extraction of text are the core problems that need to be solved in text classifi-cation. Based on this, a text classification model based on improved Continuous Bag-of-Words(CBOW)and ABiGRU is proposed. The classification model uses the word vector trained by the improved CBOW model as a word embedding layer, and then the features of the text are fully extracted through the convolutional and pooling layers of the convolutional neural network and the bidirectional gated recurrent unit neural network combined with the attention mechanism. The text feature vector is input to the softmax classifier for classification. In this paper, text categorization experiments are carried out in three datasets, the experimental results show that the proposed method has better performance than other text cate-gorization algorithms.关键词
深度学习/连续词袋模型(CBOW)/注意力机制/神经网络/文本分类Key words
deep learning/Continuous Bag-of-Word(CBOW)/attention mechanism/neural network/text classification分类
信息技术与安全科学引用本文复制引用
张宇艺,左亚尧,陈小帮..基于改进的CBOW与ABiGRU的文本分类研究[J].计算机工程与应用,2019,55(24):135-140,170,7.基金项目
广东省科技计划公益研究(No.17ZK0226). （No.17ZK0226）
