Word Embedding Based Retrieval Model for Similar Cases Recommendation
Zhao, Yifei1; Wang, Jing1; Wang, Feiyue1; Shi, Xiaobo2
2015
会议名称Chinese Automation Congress (2015)
会议录名称Proceedings of Chinese Automation Congress (2015)
会议日期2015
会议地点Wuhan
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.
关键词Internet Inquiry Case Recommendation Word Embedding Data Mining
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11708
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
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],2015.
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