Correlated Tag Learning in Topic Model

Abstract

It is natural to expect that the documents in a corpus will be correlated, and these correlations are reflected by not only the words but also the observed tags in each document. Most previous works model this type of corpus, which are called the semi-structured corpus, without considering the correlations among the tags. In this work, we develop a Correlated Tag Learning (CTL) model for semi-structured corpora based on the topic model to enable the construction of the correlation graph among tags via a logistic normal participation process. For the inference of the CTL model, we devise a variational inference algorithm to approximate the posterior. In experiments, we visualize the tag correlation graph generated by the CTL model on the DBLP corpus and for the tasks of document retrieval and classification, the correlation graph among tags is helpful to improve the generalization performance compared with the state-of-the-art baselines.