Abstract
In distributed information retrieval, answers from separate collections are combined into a single result set. However, the collections may overlap. The fact that the collections are distributed means that it is not in general feasible to prune duplicate and near-duplicate documents at index time. In this paper we introduce and analyze the grainy hash vector, a compact document representation that can be used to efficiently prune duplicate and near-duplicate documents from result lists. We demonstrate that, for a modest bandwidth and computational cost, many near-duplicates can be accurately removed from result lists produced by a cooperative distributed information retrieval system.
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Bernstein, Y., Shokouhi, M., Zobel, J. (2006). Compact Features for Detection of Near-Duplicates in Distributed Retrieval. In: Crestani, F., Ferragina, P., Sanderson, M. (eds) String Processing and Information Retrieval. SPIRE 2006. Lecture Notes in Computer Science, vol 4209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880561_10
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DOI: https://doi.org/10.1007/11880561_10
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