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中文摘要：
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%.
英文摘要：
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.
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A new query expansion approach using collocation relationships in language models for information re
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