An Improved LDA Algorithm for Text Classification

被引：0

作者：

Zhao, Dexin ^{[1
]}

He, Jinqun ^{[1
]}

Liu, Jin ^{[2
]}

机构：

[1] Tianjin Univ Technol, Tianjin Key Lab Intelligent Comp & Novel Software, Tianjin 300384, Peoples R China

[2] Tianjin Keyilong Decorat Engn Co Ltd, Tianjin 300202, Peoples R China

来源：

2014 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE, ELECTRONICS AND ELECTRICAL ENGINEERING (ISEEE), VOLS 1-3 | 2014年

关键词：

topic model; LDA; text classification;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Latent Dirichlet Allocation is a classic topic model which can extract latent topic from large data corpus. This model assumes that if a document is relevant to a topic, then all tokens in the document are relevant to that topic. In this paper, we present an algorithm called gLDA for topic text classification by adding topic-category distribution parameter to LDA, which can make the document generated from the most relevant category. Gibbs sampling is employed to conduct approximate inference, and experiment results in two datasets show the effectiveness of this method.

引用

页码：216 / +

页数：2

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