In this paper, we strive to tackle the text classification problem in the real-world setting in which texts come from a variety of domains, each with a varying amount of labeled data.
assume we have a total of N domains, N 1 labeled domains (denoted as L ) for which there is some labeled data, and N 2 unlabeled domains ( U ) for which no annotated training instances are available.