Pseudo Labled Latent Dirichlet Allocation

Abstract

In recent years, topic models have been widely used for many applications such as document summarization, document clustering etc. Labeled latent Dirichlet allocation (LLDA) was proposed based on latent Dirichlet allocation (LDA), and it regards the tags, i.e., labels, put on documents by humans as the ones expressing the contents of the documents, and uses them as supervised information to estimate latent topics of the documents. Moreover, it is reported that LLDA exceeds the ability of LDA in terms of topic estimation. However, normal documents usually do not have such tags with them, so, the use of LLDA is considerably limited.In this study, therefore, we make pseudo labels from the documents to be estimated their latent topics instead of tags put on documents by humans, and aim to make LLDA available for all documents.