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Supervised topic models leverage label information to learn discriminative latent topic representations. As collecting a fully labeled dataset is often time-consuming, semi-supervised learning is of high interest. In this paper, we present an effective semi-supervised max-margin topic model by naturally introducing manifold posterior regularization to a regularized Bayesian topic model, named LapMedLDA. The model jointly learns latent topics and a related classifier with only a small fractiondoi:10.24963/ijcai.2017/259 dblp:conf/ijcai/HuZSZZ17 fatcat:xs4t3kasvbdm3ax4jpqips26ha