Word Embedding Based Retrieval Model for Similar Cases Recommendation
Zhao, Yifei1; Wang, Jing1; Wang, Feiyue1; Shi, Xiaobo2
2015
Conference NameChinese Automation Congress (2015)
Source PublicationProceedings of Chinese Automation Congress (2015)
Conference Date2015
Conference PlaceWuhan
Abstractnone;
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.
KeywordInternet Inquiry Case Recommendation Word Embedding Data Mining
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11708
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding AuthorZhao, Yifei
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Qingdao Academy of Intelligent Industries
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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