The Auto Annotation Latent Dirichlet Allocation

被引：0
|
作者：
Xiang, Yingzhuo [1 ]
Yang, Dongmei [1 ]
Yan, Jikun [1 ]
机构：
[1] Natl Key Lab Sci & Technol Blind Signal Proc, Chengdu, Peoples R China
来源：
PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INFORMATION SCIENCES, MACHINERY, MATERIALS AND ENERGY (ICISMME 2015) | 2015年 / 126卷
关键词：
LDA; auto annotation; NLP; text modeling;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, we introduce the Auto-Annotation LDA models (aaLDA), a statistical model of non-labeled documents. This model generates the annotation of LDA automatically. We derive the annotation of LDA using a k-means methods combined with a pre-processing of the corpus. In this paper, we use aaLDA models to categorize "zhongwenshilei" corpus, which is a famous Chinese corpus. Then we make a compare with the traditional LDA methods.
引用
收藏
页码：1908 / 1911
页数：4
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