Abstract
The medical community question answering system (MCQA) which is a new kind of medical information exchange platform is becoming more and more popular. Due to the number of patients is much more than the doctors, resulting in many patients can not get timely answers to their questions. Similar question recommendation is a common approach to solve this problem. The contributions of this paper are two-fold: (1) we propose a Siamese CNN model which measure correlation between questions and answers. (2) We first apply word2vec to learn the semantic relations between words and then construct a similar question retrieval model with answers. The study above can achieve a good performance in the real MCQA data set. It shows that our method can effectively extract similar questions recommendation list, shorten user’s time to wait for an answer and improve user experience as well.
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References
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. (TOIS) 22(2), 179–214 (2004)
Surhone, L.M., Tennoe, M.T., Henssonow, S.F.: Okapi BM25. Betascript Publishing (2010)
Zhang, D., Lee, W.S.: Question classification using support vector machines. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 26–32. ACM (2003)
Nguyen, T.T., Nguyen, L.M., Shimazu, A.: Using semi-supervised learning for question classification. Inf. Media Technol. 3(1), 112–130 (2008)
Moschitti, A., Quarteroni, S., Basili, R., et al.: Exploiting syntactic and shallow semantic kernels for question answer classification. In: Annual Meeting-Association for Computational Linguistics, vol. 45, p. 776 (2007)
Goldberg, Y., Levy, O.: word2vec explained: deriving Mikolov et al’.s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 (2014)
Zhang, K., Wu, W., Wu, H., et al.: Question retrieval with high quality answers in community question answering. In: ACM International Conference on Information and Knowledge Management, pp. 371–380. ACM (2014)
Xue, X., Jeon, J., Croft, W.B.: Retrieval models for question and answer archives. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 475–482. ACM (2008)
Jeon, J., Croft, W.B., Lee, J.H.: Finding similar questions in large question and answer archives. In: ACM International Conference on Information and Knowledge Management, pp. 84–90 (2005)
Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, MA, vol. 1, p. 12 (1986)
Cai, L., Zhou, G., Liu, K., et al.: Large-scale question classification in cQA by leveraging Wikipedia semantic knowledge. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1321–1330. ACM (2011)
Bian, J., Gao, B., Liu, T.-Y.: Knowledge-powered deep learning for word embedding. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS, vol. 8724, pp. 132–148. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44848-9_9
Hu, B., Tang, B., Chen, Q., et al.: A novel word embedding learning model using the dissociation between nouns and verbs. Neurocomputing 171, 1108–1117 (2016)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
Mikolov, T., Sutskever, I., Chen, K., et al.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Acknowledgments
This work is supported by the National Science Foundation of China(No. U1633103), the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (No. CAAC-ITRB-201502).
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Cai, H., Yan, C., Yin, A., Zhao, X. (2017). Question Recommendation in Medical Community-Based Question Answering. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_23
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DOI: https://doi.org/10.1007/978-3-319-70139-4_23
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