Paragraph vector based retrieval model for similar cases recommendation
文献类型：会议论文
| 作者 | Zhao, Yifei1 ; Wang, Jing1; Wang, Fei-Yue1; Shi, Xiaobo2; Lv, Yisheng1
|
| 出版日期 | 2016 |
| 会议日期 | 12-15 June 2016 |
| 会议地点 | Guilin, China |
| 英文摘要 | Internet 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20169]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.Qingdao Academy of Intelligent Industries |
| 推荐引用方式 GB/T 7714 | Zhao, Yifei,Wang, Jing,Wang, Fei-Yue,et al. Paragraph vector based retrieval model for similar cases recommendation[C]. 见:. Guilin, China. 12-15 June 2016. |
入库方式： OAI收割
来源：自动化研究所
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