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New Technology of Library and Information Service  2015, Vol. 31 Issue (4): 10-17    DOI: 10.11925/infotech.1003-3513.2015.04.02
Current Issue | Archive | Adv Search |
Research on Query Topic Classification Method
Liu Feng1, Li Yu2, Lv Xueqiang2, Li Zhuo2
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
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[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.

Key wordsQuery topic classification      Pseudo feedback      Query expansion      Vector space compression algorithm     
Received: 19 September 2014      Published: 21 May 2015
:  TP391  

Cite this article:

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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