Computer Science ›› 2015, Vol. 42 ›› Issue (6): 18-22.doi: 10.11896/j.issn.1002-137X.2015.06.004

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Query Expansion Based on Classification Model

LI Wei-yin, SHI Yu-long, CHEN Jie and SHI Chong-yang   

  • Online:2018-11-14 Published:2018-11-14

Abstract: As a key component of query optimization,query expansion plays an important role in improving the perfor-mance of information retrieval systems.Traditional query expansion methods on pseudo-relevance feedback improve the performance of retrieval to some extent.However,the selected expansion terms will also include some irrelevant ones,which leads to adverse effect.In this paper,a novel query expansion method based on classification model was proposed.Combining with statistical information and various features of the candidate expansion terms,this method employs Naive Bayes classification model to reselect the candidate expansion terms so as to further filter the irrelevant ones.Experimental results on TREC 2013 datasets show that the proposed query expansion method can efficiently improve the precision and recall of user queries.

Key words: Query expansion,Classification model,Information retrieval,Pseudo relevance feedback

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