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《Technology Intelligence Engineering》 2018-03
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The Recommendation Approach Based on Term Extraction and Graduation Matching

GU Yingzhi;DONG Cheng;PEI Bingbing;DU Yongping;Faculty of Information Technology, Beijing University of Technology;Institute of Scientific and Technical Information of China;Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content,SAPPRFT;  
Text recommendation is an important technology in the field of Natural Language Processing. In order to recommend the project guidelines to the researcher, this paper uses terminology to represent the text features and gives the recommendation based on the graduation matching. The rule of part of speech and syntactic information are used for term extraction and the candidate terms are filtered by statistical methods, such as C-value, SCP(Symmetrical Conditional Probability) and so on, so as to improve the extraction quality. The graduation based matching between the guidelines and researchers' terminology is used to measure the similarity, and then to achieve the personalized recommendation. The experiments are implemented on the 42 project guidelines published by the public service platform in 2017. The results show that the C-value+SCP based method achieves better term extraction quality and the personalized recommendation precision is up to 80%.
【Fund】: 科技部创新方法工作专项(2015IM020500);; 北京市自然科学基金资助项目(4153058)
【CateGory Index】: TP391.3
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