A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback

Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select novel pseudo-relevant documents based on Lavrenko's relevance model approach. The main idea is to use overlapping clusters to find dominant documents for the initial retrieval set, and to repeatedly use these documents to emphasize the core topics of a query. The proposed resampling method can skip some documents in the initial high-ranked documents and deterministically construct overlapping clusters as sampling units. The hypothesis behind using overlapping clusters is that a good representative document for a query may have several nearest neighbors with high similarities, participating in several different clusters. Experimental results on large-scale web TREC collections show significant improvements over the baseline relevance model. To justify the proposed approach, we examine the relevance density and redundancy ratio of feedback documents. A higher relevance density will result in greater retrieval accuracy, ultimately approaching true relevance feedback. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback.