摘要
文本分类技术是信息过滤、搜索引擎等领域的基础,是当下研究热点之一。本文在介绍文本分类相关概念、深度学习相关模型的基础上,通过分析传统文本分类方法存在的不足,提出基于变分自编码器模型和深度置信网络模型(VAE-DBN)的双模型融合的文本分类方法。通过在相关语料集上的对比验证,表明该双模型方法能有效提高文本分类的准确性。
Text categorization technology is the foundation of information filtering,search engine and other fields,and is one of current research hot-spots.Based on the introduction of text classification related concepts and deep learning related models,this paper presents a dual-model text classification method based on the variational autoencoder model and the deep belief network model(VAE-DBN)by analyzing the shortcomings of the traditional text classification methods.By comparing and verifying the corpus,the results show that the dual-model method can effectively improve the accuracy of text categorization.
作者
王玮
WANG Wei(Dept.of Graduate,Academy of Military Sciences,Beijing 100091,China;31608 Force,Xiamen 361025,China)
出处
《计算机与现代化》
2018年第12期77-84,105,共9页
Computer and Modernization
关键词
变分自编码器
深度置信网络
文本分类
variational autoencoder
deep belief network
text categorization
