Abstract
Re-ranking (RR) and Cluster-based Retrieval (CR) have been polar methods for improving retrieval effectiveness by using inter-document similarities. However, RR and CR improve precision and recall respectively, not simultaneously. Thus, the improvement through RR and CR may be different according to whether a query is recall-deficient or not. However, previous researchers missed out this point, and separately investigated individual approaches, causing a limited improvement. To reflect all of positive effects by RR and CR, this paper proposes RCR, the re-ranking with cluster-based retrieval where RR is applied to initially-retrieved results of CR. Experimental results show that RCR significantly improves the baseline, while CR or RR sometimes does not significantly improve the baseline.
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© 2008 Springer-Verlag Berlin Heidelberg
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Na, SH., Kang, IS., Lee, JH. (2008). Structural Re-ranking with Cluster-Based Retrieval. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_74
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DOI: https://doi.org/10.1007/978-3-540-78646-7_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78645-0
Online ISBN: 978-3-540-78646-7
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