Utilizing inter-passage and inter-document similarities for reranking search results

We present a novel language-model-based approach to reranking search results; that is, reordering the documents in an initially retrieved list so as to improve precision at top ranks. Our model integrates whole-document information with that induced from passages. Specifically, interpassage, inter-document, and query-based similarities, which constitute a rich source of information, are combined in our model. Empirical evaluation shows that the precision-at-top-ranks performance of our model is substantially better than that of the initial ranking upon which reranking is performed. Furthermore, the performance is substantially better than that of a commonly used passage-based document ranking method that does not exploit inter-item similarities. Our model also generalizes and outperforms a recently proposed reranking method that utilizes interdocument similarities, but which does not exploit passage-based information. Finally, the model's performance is superior to that of a state-of-the-art pseudo-feedback-based retrieval approach.