A text classification model constructed by Latent Dirichlet Allocation and Deep Learning

被引：0
|
作者：
Liu, Yu [1 ]
Jin, Zhengping [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
来源：
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON MECHATRONICS, MATERIALS, CHEMISTRY AND COMPUTER ENGINEERING 2015 (ICMMCCE 2015) | 2015年 / 39卷
关键词：
text classification; latent Dirichlet allocation; deep learning; Gibbs sampling;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we proposed a mixed model of text classification constructed by latent dirichlet allocation and deep learning. The model present that a text will be represent as a vector computing by latent dirichlet allocation algorithm, and this vector is probabilistic vector of corresponding topic words space. Then we input these topic vectors into a deep learning framework for computing nonlinear relationship of each vector. Finally, we constructed a text classification system. The proposed model achieves a higher accuracy when compared with other current popular algorithms, such as SVM, KNN and TFIDF.
引用
收藏
页码：2501 / 2504
页数：4
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