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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References
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006). JMLR.org
Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: Advances in Neural Information Processing Systems, pp. 137–144 (2007)
Dai, W., Yang, Q., Xue, G.-R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200. ACM (2007)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2066–2073. IEEE (2012)
Huang, J., Gretton, A., Borgwardt, K.M., Schölkopf, B., Smola, A.J.: Correcting sample selection bias by unlabeled data. In: Advances in Neural Information Processing Systems, pp. 601–608 (2007)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: 2013 IEEE International Conference on Computer Vision, ICCV, pp. 2200–2207. IEEE (2013)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1417 (2014)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Salton, G., Buckley, C.: Term-Weighting Approaches in Automatic Text Retrieval. Pergamon Press Inc., Oxford (1988)
Stewart, R., Ermon, S.: Label-free supervision of neural networks with physics and domain knowledge. In: AAAI, pp. 2576–2582 (2017)
Tan, B., Song, Y., Zhong, E., Yang, Q.: Transitive transfer learning. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1155–1164. ACM (2015)
Tan, B., Zhang, Y., Pan, S.J., Yang, Q.: Distant domain transfer learning. In: AAAI, pp. 2604–2610 (2017)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Computer Vision and Pattern Recognition, CVPR, vol. 1, p. 4 (2017)
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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