Multi-Label Topic Model Conditioned on Label Embedding
Lin Tang, Lin Liu, Jianhou Gan · 2019
In most real-world document collections, there are various types of labels that usually carry context information, such as label hierarchies or textual descriptions. Nonetheless, the commonly-used approaches to modeling text corpora ignore this information. Label embedding can reflect more extensive label context information and have a capability of leveraging various sources of information. In this paper, we propose a multi-label topic model conditioned on label embedding, which incorporate label embedding into the generative process of multi-label topic models in text domain, so as to improve documents classification accuracy and topic quality. By introducing Dirichlet-multinomial regression (DMR) framework into a multi-label topic model called Labeled LDA(LLDA), our model apply an exponential priori constructed previously with label embedding on the hyperparameters of document-label distribution, which reflects the effects of label embedding on label probability distribution. The experimental results demonstrate the potential of our model through an exploration of a standard document dataset.