1Smart City College, Beijing UnionUniversity, Beijing 100101, China 2College of Urban Rail Transit and Logistics, Beijing Union University, Beijing 100101, China 3Beijing China-Power Information Technology Co., LTD, Beijing 100192, China
[Objective] This paper addresses the issues facing labeled data in the recognition of scenic spots.[Methods] We proposed an improved knowledge transfer algorithm for entity recognition and used datasets from the People’s Daily to evaluate our new model.[Results] Our method’s accuracy was 1.62% higher than the model using all labeled data.[Limitations] More research is needed to examine the expansion of samples.[Conclusions] The proposed method uses less labeled data in entity recognition and provides better technical support for tourism recommendation.
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