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

页码：1908 / 1911

页数：4

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