We present an efficient query evaluation method based on a two level approach: at the first level, our method iterates in parallel over query term postings and identifies candidate documents using an approximate evaluation taking into account only partial information on term occurrences and no query independent factors; at the second level, promising candidates are fully evaluated and their exact scores are computed. The efficiency of the evaluation process can be improved significantly using dynamic pruning techniques with very little cost in effectiveness. The amount of pruning can be controlled by the user as a function of time allocated for query evaluation. Experimentally, using the TREC Web Track data, we have determined that our algorithm significantly reduces the total number of full evaluations by more than 90%, almost without any loss in precision or recall.At the heart of our approach there is an efficient implementation of a new Boolean construct called WAND or Weak AND that might be of independent interest.

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When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.

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