Or Levi
Ido Guy
ABSTRACT
We address the long standing challenge of selective cluster-based retrieval; namely, deciding on a per-query basis whether to apply cluster-based document retrieval or standard document retrieval. To address this classification task, we propose a few sets of features based on those utilized by the cluster-based ranker, query-performance predictors, and properties of the clustering structure. Empirical evaluation shows that our method outperforms state-of-the-art retrieval approaches, including cluster-based, query expansion, and term proximity methods.
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Index Terms
- Selective Cluster-Based Document Retrieval
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