Query Expansion of Pseudo Relevance Feedback Based on Feature Terms Extraction and Correlation Fusion
Feng Ping1, Huang Mingxuan2
1. Electronic Information and Control Engineering Department, Guangxi University of Technology, Liuzhou 545006, China;
2. Department of Math and Computer Science, Guangxi College of Education, Nanning 530023, China
Aiming at the term mismatch issues of existing information retrieval systems, a novel query expansion algorithm of pseudo relevance feedback is proposed based on feature terms extraction and correlation fusion. At the same time, a new computing method for weights of expansion terms is also given. The algorithm can extract feature terms related to original query from the n chapter top-ranked retrieved local documents, and then identify those feature terms as final expansion terms according to the frequency of each feature term appeared in the local documents and the correlation between each feature term and the entire original query for query expansion. The results of the experiment show that the method is effective,and it can enhance and improve the performance of information retrieval.
冯平, 黄名选. 特征词抽取和相关性融合的伪相关反馈查询扩展[J]. 现代图书情报技术, 2011, 27(1): 52-56.
Feng Ping, Huang Mingxuan. Query Expansion of Pseudo Relevance Feedback Based on Feature Terms Extraction and Correlation Fusion. New Technology of Library and Information Service, 2011, 27(1): 52-56.
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