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. © 2011 Springer-Verlag.
CITATION STYLE
Fu, X., Chen, D., Guo, X., & Wang, C. (2011). Query classification based on index association rule expansion. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6988 LNCS, pp. 311–318). https://doi.org/10.1007/978-3-642-23982-3_38
Mendeley helps you to discover research relevant for your work.