《计算机应用研究》|Application Research of Computers

基于卷积神经网络和贝叶斯分类器的句子分类模型

Sentence classification model based on convolution neural network and Bayesian classifier

免费全文下载 (已被下载 次)  
获取PDF全文
作者 李文宽,刘培玉,朱振方,刘文锋
机构 1.山东师范大学 信息科学与工程学院,济南 250014;2.山东省分布式计算机软件新技术重点实验室,济南 250014;3.山东交通学院 信息科学与电气工程学院,济南 250014;4.菏泽学院 计算机学院,山东 菏泽 274015
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2020)02-003-0333-04
DOI 10.19734/j.issn.1001-3695.2018.07.0525
摘要 针对传统句子分类模型存在特征提取过程复杂且分类准确率较低等不足,利用当下流行的基于深度学习模型的卷积神经网络在特征提取上的优势,结合传统句子分类方法提出一种基于卷积神经网络和贝叶斯分类器的句子分类模型。该模型首先利用卷积神经网络提取文本特征,其次利用主成分分析法对文本特征进行降维,最后利用贝叶斯分类器进行句子分类。实验结果表明在康奈尔大学公开的影评数据集和斯坦福大学情感分类数据集上,所提模型优于只使用深度学习的模型或传统句子分类模型。
关键词 深度学习; 句子分类; 卷积神经网络; 主成分分析法; 贝叶斯分类器
基金项目 国家自然科学基金资助项目(61373148)
国家青年自然科学基金资助项目(61502151)
山东省社科规划项目(17CHLJ18,17CHLJ33,17CHLJ30)
山东省自然科学基金资助项目(ZR2014FL010)
山东省教育厅基金资助项目(J15LN34)
本文URL http://www.arocmag.com/article/01-2020-02-003.html
英文标题 Sentence classification model based on convolution neural network and Bayesian classifier
作者英文名 Li Wenkuan, Liu Peiyu, Zhu Zhenfang, Liu Wenfeng
机构英文名 1.School of Information Science & Engineering,Shandong Normal University,Jinan 250014,China;2.Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology,Jinan 250014,China;3.School of Information Science & Electric Engineering,Shandong Jiaotong University,Jinan 250014,China;4.School of Computer Science,Heze University,Heze Shandong 274015,China
英文摘要 The traditional sentence classification model has many disadvantages such as complex feature extraction process and low classification accuracy. This paper used the advantages of the popular deep learning model based convolutional neural network in feature extraction, combined with the traditional sentence classification method, proposed a sentence classification model based on convolutional neural network and Bayesian classifier. The model first used convolutional neural network to extract text features, and secondly used principal component analysis method to reduce the dimensionality of text features. Finally, Bayesian classifier were used to classify sentences. The experimental results show that on Cornell University′s public film review dataset and Stanford Sentiment Treebank dataset, the proposed model is superior to the model using only deep learning or the traditional sentence classification model.
英文关键词 deep learning; sentence classification; convolutional neural network(CNN); principal component analysis; Bayesian classifier
参考文献 查看稿件参考文献
 
收稿日期 2018/7/16
修回日期 2018/8/27
页码 333-336,341
中图分类号 TP391.1
文献标志码 A