In this work, we present a novel supervised representation learning approach to discover a latent representation of words which is not only generalizable across domains but also informative to the classification task.
we propose a hier-archical multinomial Naive Bayes model with latent word cluster variables to perform supervised word clustering on labeled documents from both domains.