Word Embedding Based Retrieval Model for Similar Cases Recommendation
文献类型：会议论文
| 作者 | Zhao, Yifei1 ; Wang, Jing1; Wang, Feiyue1; Shi, Xiaobo2
|
| 出版日期 | 2015 |
| 会议日期 | 2015 |
| 会议地点 | Wuhan |
| 关键词 | Internet Inquiry Case Recommendation Word Embedding Data Mining |
| 英文摘要 | none;
Similar cases recommendation is more and more
popular in the internet inquiry. There have been lots of cases
which have been solved perfectly, and recommending them to
similar inquiries can not only save the patients’ waiting time,
but also giving more good references. However, the inquiry
platform cannot understand the diversity of description, i.e. the
same meaning with different description. This may shield some
cases with very high quality answers. In this paper, based on
deep learning, we proposed a retrieval model combining word
embedding with language models. We use word embedding to
solve the problem of description diversity, and then
recommend the similar cases for the inquiries. The
experiments are based on the data from ask.39.net, and the
results show that our methods outperform the state-of-art
methods. |
| 会议录 | Proceedings of Chinese Automation Congress (2015)
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/11708]  |
| 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 |
| 通讯作者 | Zhao, Yifei |
| 作者单位 | 1.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, Feiyue,et al. Word Embedding Based Retrieval Model for Similar Cases Recommendation[C]. 见:. Wuhan. 2015. |
入库方式： OAI收割
来源：自动化研究所
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