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HealthQA: A Chinese QA Summary System for Smart Health

  • Conference paper
Smart Health (ICSH 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8549))

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Abstract

Although online health expert QA services can provide high quality information for health consumers, there is no Chinese question answering system built on knowledge from existing expert answers, leading to duplicated efforts of medical experts and reduced efficiency. To address this issue, we develop a Chinese QA system for smart health (HealthQA), which provides timely, automatic and valuable QA service. Our HealthQA collects diabetes expert question answer data from three major QA websites in China. We develop a hierarchical clustering method to group similar questions and answers, an extended similarity evaluation algorithm for retrieving relevant answers and a ranking based summarization for representing the answer. ROUGE and manual tests show that our system significantly outperforms the search engine.

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References

  1. Athenikos, S.J., Han, H.: Biomedical question answering: A survey. Computer Methods and Programs in Biomedicine 99(1), 1–24 (2010)

    Article  Google Scholar 

  2. START, http://start.csail.mit.edu/

  3. Kaisser, M.: The QuALiM question-answering demo: Supplementing answers with paragraphs drawn from Wikipedia. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session, pp. 32–35. Association for Computational Linguistics (2008)

    Google Scholar 

  4. Radev, D., Fan, W., Qi, H., Wu, H., Grewal, A.: Probabilistic question answering on the web. Journal of the American Society for Information Science and Technology 56(6), 571–583 (2005)

    Article  Google Scholar 

  5. Roussinov, D., Fan, W., Robles-Flores, J.: Beyond keywords: Automated question answering on the web. Communications of the ACM 51(9), 60–65 (2008)

    Article  Google Scholar 

  6. Lee, M., Cimino, J., Zhu, H.R., et al.: Beyond information retrieval—medical question answering. In: AMIA Annual Symposium Proceedings 2006, p. 469. American Medical Informatics Association (2006)

    Google Scholar 

  7. Cao, Y., Liu, F., Simpson, P., Antieau, L., Bennett, A., Cimino, J., Yu, H.: AskHERMES: An online question answering system for complex clinical questions. Journal of Biomedical Informatics 44(2), 277–288 (2011)

    Article  Google Scholar 

  8. Cairns, B.L., Nielsen, R.D., Masanz, J.J., Martin, J.H., Palmer, M.S., Ward, W.H., Savova, G.K.: The MiPACQ clinical question answering system. In: AMIA Annual Symposium Proceedings, vol. 2011, p. 171. American Medical Informatics Association (2011)

    Google Scholar 

  9. Ko, J., Si, L., Nyberg, E., Mitamura, T.: Probabilistic models for answer-ranking in multilingual question-answering. ACM Transactions on Information Systems (TOIS) 28(3), 16 (2010)

    Article  Google Scholar 

  10. Peng, X., Chen, Y., Huang, Z.: A Chinese Question Answering system using web service on restricted domain. In: 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI), vol. 1, pp. 350–353. IEEE (2010)

    Google Scholar 

  11. Zhang, H., Zhu, L., Xu, S., Li, W.: XML-Based Document Retrieval in Chinese Diseases Question Answering System. In: Park, J.J.(J.H.), Adeli, H., Park, N., Woungang, I., (eds.) Mobile, Ubiquitous, and Intelligent Computing. LNEE, vol. 274, pp. 211–217. Springer, Heidelberg (2014)

    Google Scholar 

  12. Meadow, C.T., Boyce, B.R., Kraft, D.H.: Text Information Retrieval Systems, 2nd edn. Academic Press (2000)

    Google Scholar 

  13. Lee, M.C.: A novel sentence similarity measure for semantic-based expert systems. Expert Systems with Applications 38(5), 6392–6399 (2011)

    Article  Google Scholar 

  14. Caropreso, M.F., Matwin, S., Sebastiani, F.: A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. In: Text Databases and Document Management: Theory and Practice, pp. 78–102 (2001)

    Google Scholar 

  15. Hassan, S., Mihalcea, R.: Semantic Relatedness Using Salient Semantic Analysis. AAAI (2011)

    Google Scholar 

  16. Guo, W., Diab, M.: A simple unsupervised latent semantics based approach for sentence similarity. In: Proceedings of the First Joint Conference on Lexical and Computational, pp. 586–590. Association for Computational Linguistics (2012)

    Google Scholar 

  17. Li, Y., McLean, D., Bandar, Z.A., et al.: Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering 18(8), 1138–1150 (2006)

    Article  Google Scholar 

  18. Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336. ACM (1998)

    Google Scholar 

  19. Mittal, V., Kantrowitz, M., Goldstein, J., et al.: selecting text spans for document summaries: Heuristics and metrics (1999)

    Google Scholar 

  20. Goldstein, J., Mittal, V., Carbonell, J., et al.: Multi-document summarization by sentence extraction. In: Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic Summarization, vol. 4, pp. 40–48. Association for Computational Linguistics (2000)

    Google Scholar 

  21. Song, W., Yu, Q., Xu, Z., et al.: Multi-aspect query summarization by composite query. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334. ACM (2012)

    Google Scholar 

  22. Wang, D., Zhu, S., Li, T., et al.: Multi-document summarization using sentence-based topic models. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 297–300. Association for Computational Linguistics (2009)

    Google Scholar 

  23. Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop, pp. 74–81 (2004)

    Google Scholar 

  24. Liu, Y., Li, S., Cao, Y., et al.: Understanding and summarizing answers in community-based question answering services. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 497–504. Association for Computational Linguistics (2008)

    Google Scholar 

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Author information

Authors and Affiliations

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

    Yanshen Yin, Yong Zhang, Yan Zhang, Chunxiao Xing & Hsinchun Chen

  2. Artificial Intelligence Lab, University of Arizona, USA

    Xiao Liu & Hsinchun Chen

Authors
  1. Yanshen Yin
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  2. Yong Zhang
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  3. Xiao Liu
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  4. Yan Zhang
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  5. Chunxiao Xing
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  6. Hsinchun Chen
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Editor information

Editors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Xiaolong Zheng

  2. The University of Arizona, Tucson, AZ, USA and Chinese Academy of Sciences,, Beijing, China

    Daniel Zeng

  3. The University of Arizona, Tucson, AZ, USA and Tsinghua University, Beijing,, China

    Hsinchun Chen

  4. Tsinghua University, Beijing, China

    Yong Zhang  & Chunxiao Xing  & 

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Daniel B. Neill

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Yin, Y., Zhang, Y., Liu, X., Zhang, Y., Xing, C., Chen, H. (2014). HealthQA: A Chinese QA Summary System for Smart Health. In: Zheng, X., Zeng, D., Chen, H., Zhang, Y., Xing, C., Neill, D.B. (eds) Smart Health. ICSH 2014. Lecture Notes in Computer Science, vol 8549. Springer, Cham. https://doi.org/10.1007/978-3-319-08416-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-08416-9_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08415-2

  • Online ISBN: 978-3-319-08416-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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