Abstract
Transfer learning has emerged as a new learning technique facilitating an improved learning result of one task by integrating the well learnt knowledge from another related task. While much research has been devoted to develop the transfer learning algorithms in the field of long text analysis, the development of the transfer learning techniques over the short texts still remains challenging. The challenge of short text data analysis arises due to its sparse nature, noise words, syntactical structure and colloquial terminologies used. In this paper, we propose AutoTL(Automatic Transfer Learning), a transfer learning framework in short text analysis with automatic training data selection and no requirement of data priori probability distribution. In addition, AutoTL enables an accurate and effective learning by transferring the knowledge automatically learnt from the online information. Our experimental results confirm the effectiveness and efficiency of our proposed technique.
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Chu, Y., Wang, Z., Chen, M., Xia, L., Wei, F., Cai, M. (2015). Transfer Learning in Large-Scale Short Text Analysis. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_45
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DOI: https://doi.org/10.1007/978-3-319-25159-2_45
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