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Domain Supervised Deep Learning Framework for Detecting Chinese Diabetes-Related Topics

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Book cover Database Systems for Advanced Applications (DASFAA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10828))

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Abstract

As millions of people are diagnosed with diabetes every year in China, many diabetes-related websites in Chinese provide news and articles. However, most of the online articles are uncategorized or lack a clear or unified topic, users often cannot find their topics of interest effectively and efficiently. The problem of health text classification on Chinese websites cannot be easily addressed by applying existing approaches, which have been used for English documents, in a straightforward manner. To address this problem and meet users’ demand for diabetes-related information needs, we propose a Chinese domain lexicon, adopt some professional diabetes topic explanations as domain knowledge and incorporate them into deep learning approach to form our topic classification framework. Our experiments using real datasets showed that the framework significantly achieved a higher effectiveness and accuracy in categorizing diabetes-related topics than most of the state-of-the-art benchmark approaches. Our experimental analysis also revealed that some health websites provided some incorrect or misleading category information.

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Acknowledgments

This work was supported by NSFC (91646202), NSSFC (15CTQ028), the National High-tech R&D Program of China (SS2015AA020102), Research/Project 2017YB142 supported by Ministry of Education of The People’s Republic of China, the 1000-Talent program and Tsinghua University Initiative Scientific Research Program.

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Authors and Affiliations

  1. Research Institute of Information Technology, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Institute of Internet Industry, Tsinghua University, Beijing, 100084, China

    Xinhuan Chen, Yong Zhang, Kangzhi Zhao, Qingcheng Hu & Chunxiao Xing

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  1. Xinhuan Chen
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  2. Yong Zhang
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  3. Kangzhi Zhao
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  4. Qingcheng Hu
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  5. Chunxiao Xing
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Corresponding author

Correspondence to Xinhuan Chen .

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Editors and Affiliations

  1. Simon Fraser University, Burnaby, BC, Canada

    Jian Pei

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

  3. University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

  4. University of Western Australia, Crawley, WA, Australia

    Jianxin Li

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Chen, X., Zhang, Y., Zhao, K., Hu, Q., Xing, C. (2018). Domain Supervised Deep Learning Framework for Detecting Chinese Diabetes-Related Topics. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-91458-9_4

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  • Online ISBN: 978-3-319-91458-9

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