Distant domain adaptation for text classification

Abstract

Text classification becomes a hot topic nowadays. In reality, the training data and the test data may come from different distributions, which causes the problem of domain adaptation. In this paper, we study a novel learning problem: Distant Domain Adaptation for Text classification (DDAT). In DDAT, the target domain can be very different from the source domain, where the traditional transfer learning methods do not work well because they assume that the source and target domains are similar. To solve this issue we propose a Selective Domain Adaptation Algorithm (SDAA). SDAA iteratively selects reliable instances from the source and intermediate domain to bridge the source and target domains. Extensive experiments show that SDAA has state-of-the-art classification accuracies on the test datasets.