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.
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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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Zhu, Z., Li, Y., Li, R., Gu, X. (2018). Distant Domain Adaptation for Text Classification. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_5
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DOI: https://doi.org/10.1007/978-3-319-99365-2_5
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