Paragraph vector based retrieval model for similar cases recommendation
Zhao, Yifei1; Wang, Jing1; Wang, Fei-Yue1; Shi, Xiaobo2; Lv, Yisheng1
Conference Name2016 12th World Congress on Intelligent Control and Automation
Conference Date12-15 June 2016
Conference PlaceGuilin, China
AbstractInternet inquiry is playing an increasingly important role as the complement of the traditional medical service system, especially the similar cases recommendation. It can not only save the patients' waiting time, but also make use of the historical resources, for many cases with the same purpose have been solved perfectly. However, because of the diversity and non-standard of the patients' descriptions, the inquiry platform cannot find the cases with similar semantic easily. Most traditional retrieval methods require the overlap of two sentences, and this is not suitable with the diversity and non-standard descriptions. In this paper, we try to utilize the sentences' semantic representation in a continuous space to understand the cases, and then recommend the similar cases. We also incorporate it into query likelihood language models, trying to get better results. Our experimental data are all collected from a real internet inquiry platform, and the results show that our methods significantly outperform the state-of-the-art translation based methods for similar cases recommendation.
Document Type会议论文
Affiliation1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.Qingdao Academy of Intelligent Industries
Recommended Citation
GB/T 7714
Zhao, Yifei,Wang, Jing,Wang, Fei-Yue,et al. Paragraph vector based retrieval model for similar cases recommendation[C],2016.
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