Abstract
Query classification can improve the query results of search engine, but the existing query classification methods which use extra web resources to enrich query features easily result in high delay. In this paper, a query classification based on index association rule expansion (IARE-QC) is proposed. IARE-QC uses an index based query classification framework to reduce the response time through transforming the query classification problem in online phase to the equivalent index term classification in offline phase. Moreover, in order to get more accurate feature enrichment of index term, we propose a novel algorithm which called index association expansion based on similarity voting (IARE-SV) to determine the category labels of index term. The experiment results on the search engine simulation environment show that IARE-SV can get much better query classification performance than the common simple voting (SV) method.
This work is supported in part by National Natural Science Foundation of China (60903114, 61003271, 61001185), Natural Science Foundation of Guangdong Province (7301329), Scienceand Technology Foundation of Shenzhen City (JC201005280463A).
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Fu, X., Chen, D., Guo, X., Wang, C. (2011). Query Classification Based on Index Association Rule Expansion. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23982-3_38
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DOI: https://doi.org/10.1007/978-3-642-23982-3_38
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