摘要
LDA(Latent Dirichlet Allocation)模型是近年来提出的一种能够提取文本隐含主题的非监督学习模型.通过在传统LDA模型中融入文本类别信息,文中提出了一种附加类别标签的LDA模型(Labeled-LDA).基于该模型可以在各类别上协同计算隐含主题的分配量,从而克服了传统LDA模型用于分类时强制分配隐含主题的缺陷.与传统LDA模型的实验对比表明:基于Labeled-LDA模型的文本分类新算法可以有效改进文本分类的性能,在复旦大学中文语料库上micro-F1提高约5.7%,在英文语料库20newsgroup的comp子集上micro-F1提高约3%.
LDA（Latent Dirichlet Allocation） is a recently proposed model which extracts latent topics from text data. In this paper, Labeled-LDA is proposed to enhance the traditional LDA to integrate the class information. Based on Labeled-LDA, a new algorithm is introduced to figure out the latent topics＇ quantities of each class synergistical]y. In such a way, Labeled-LDA model avoids compulsive allocation behaviors of the traditional LDA when it is used as a component in classification frame. Experiments on fudan corpus and the comp subset of 20newsgrop corpus show the new method can improve text classification effectiveness： On micro_F1 measure, it approaches an improvement of 5.7% on fudan corpus and 3% on the comp subset of 20newsgrop corpus.
出处
《计算机学报》
EI
CSCD
北大核心
2008年第4期620-627,共8页
Chinese Journal of Computers
基金
国家自然科学基金项目(60773027)
国家自然科学基金重点项目(60736044)
国家“八六三”高技术研究发展计划重点项目基金(2006AA010108)资助~~
关键词
文本分类
图模型
隐含狄利克雷分配
变分推断
text classification
graphical model
Latent Dirichlet Allocation （LDA）
variationalinference
