基于深度学习的文本分类技术研究进展

随着深度学习技术的快速发展，许多研究者尝试利用深度学习来解决文本分类问题，特别是在卷积神经网络和循环神经网络方面，出现了许多新颖且有效的分类方法。对基于深度神经网络的文本分类问题进行分析，介绍卷积神经网络、循环神经网络、注意力机制等方法在文本分类中的应用和发展，分析多种典型分类方法的特点和性能，从准确率和运行时间方面对基础网络结构进行比较，表明深度神经网络较传统机器学习方法在用于文本分类时更具优势，其中卷积神经网络具有优秀的分类性能和泛化能力。在此基础上，指出当前深度文本分类模型存在的不足，并对未来的研究方向进行展望。

Research Progress of Text Classification Technology Based on Deep Learning

With the rapid development of deep learning technology,many researchers have tried to utilize deep learning to solve the text classification problems.Especially in terms of the Convolutional Neural Network(CNN)and Recurrent Neural Network(RNN),many novel and effective classification methods have been proposed.This paper analyzes the problem of text classification based on Deep Neural Network(DNN)and introduces the application and development of the CNN,RNN,attention mechanism and other methods in text classification.Then it analyzes the characteristics and performance of multiple text classification methods based on deep learning,and compares the basic network structures in terms of accuracy and elapsed time,and shows that DNN methods outperform the traditional machine learning methods,and among them,CNN provides better classification performance and generalization ability.On this basis,this paper points out the deficiencies of the existing deep models for text classification,and prospects the direction of future research.