Abstract
Pseudo-relevance feedback has shown to be an effective method in many information retrieval tasks. Various criteria have been proposed to rank terms extracted from the top ranked document of the initial retrieval results. However, most existing methods extract terms individually and do not consider the impacts of relationships among terms and their combinations. In this study, we first re-examine this assumption and show that combinations of terms may heavily impact the final results. We then present a novel clustering based method to select expansion terms as a whole set. The main idea is to use first simultaneously cluster terms and documents using non-negative matrix factorization, and then use the Maximum Relevance and Minimum Redundancy criteria to select terms based on their clusters, term distributions, and other features. Experimental results on serval TREC collections show that our proposed method significantly improves performances.
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Wu, Y., Zhang, Q., Zhou, Y., Huang, X. (2010). Pseudo-Relevance Feedback Based on mRMR Criteria. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_20
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DOI: https://doi.org/10.1007/978-3-642-17187-1_20
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