STRUCTURAL RE-RANKING WITH CLUSTER-BASED RETRIEVAL

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.