Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows.
Bruza, Peter D.
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This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term re- lationships in query language modeling, which takes into account terms implicitly associated with di®erent subsets of query terms. Existing ap- proaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associa- tions and also su®er from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback docu- ments that are segmented into variable length chunks via multiple sliding windows of di®erent sizes. Extensive experiments have been conducted on various TREC collections and our approach signi¯cantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.