Query Expansion of Pseudo Relevance Feedback Based on Matrix-Weighted Association Rules Mining

Huang, Mingxuan; Yan, Xiaowei; Zhang, Shichao

doi:10.3724/sp.j.1001.2009.03368

Cited by 5 publications

(1 citation statement)

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“…We consider implementing it with the association rule mining of words [14]. In order to getting the association support of the index term and the expanding feature word on the search engine's index, here we propose an index term expansion based on the association support.…”

Section: Index Term Expansion Based On Association Supportmentioning

confidence: 99%

Query Classification Based on Index Association Rule Expansion

Chen

Guo

et al. 2011

Web Information Systems and Mining

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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.

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Section: Index Term Expansion Based On Association Supportmentioning

confidence: 99%