关键词: 隐藏狄利克雷分配（LDA）, 监督主题模型, 文档聚类, 作者预测

Abstract: Most of supervised and semi-supervised latent Dirichlet allocation (LDA) models add metadata based on DSTM (downstream supervised topic model) or USTM (upstream supervised topic model) methods, which can improve the capabilities of topics extraction and dimension reduction. However those models can not analyze academic documents which have more than one kind of metadata. Based on the research on the LDA model and its modifications, this paper proposes a new LDA model namely author & references topic (ART) model. The ART model defines the generation process of authors and references by USTM and DSTM which makes the model be able to analyze documents both with authors and references information. In the experiment, Stochastic EM Sampling method is used to learn the parameters of ART model and the ART model is compared with Labeled LDA and DMR models. The experimental results show that the ART model not only has efficient capabilities of academic documents topic extraction and clustering, but also can give an accurate prediction of authors for a new document.

Key words: latent Dirichlet allocation (LDA), supervised topic model, documents clustering, predicting authors