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Distant Domain Adaptation for Text Classification

  • Zhenlong Zhu
  • Yuhua Li
  • Ruixuan Li
  • Xiwu Gu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

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.

Keywords

Text classification Distant domain adaptation Transfer learning 

Notes

Acknowledgements

This work is supported by the National Key Research and Development Program of China under grants 2016QY01W0202 and 2016YFB0800402, National Natural Science Foundation of China under grants 61572221, U1401258, 61433006 and 61502185. Guangxi High level innovation Team in Higher Education Institutions Innovation Team of ASEAN Digital Cloud Big Data Security and Mining Technology.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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