1 First Research Institute of the Ministry of Public Security of P.R.C, Beijing 100048, China;
2 Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China
[Objective] Expand the queries to get the query topic. [Methods] Get the query expansion text by using the pseudo-feedback technology, extract the text features and combine them by the proposed partial matching rules and vector space compression algorithm. In the end, the query topic classification can be done by the Cosine Include Angle and SVM. [Results] The precision can reach 90.34%, the recall rate is 89.34%, the F value is 89.67% and the accuracy is 89.24%. [Limitations] Online processing efficiency is not high because of expanding the queries using the searching results. [Conclusions] The proposed method is effective in query topic classification. Using the machine learning method can get the better experimental results than the Cosine Include Angle and it is significative for improving the quality of search engine.
刘峰, 李煜, 吕学强, 李卓. 查询主题分类方法研究[J]. 现代图书情报技术, 2015, 31(4): 10-17.
Liu Feng, Li Yu, Lv Xueqiang, Li Zhuo. Research on Query Topic Classification Method. New Technology of Library and Information Service, 2015, 31(4): 10-17.
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