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

页码：2501 / 2504

页数：4

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