Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval (2005)
| Venue: | In SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval |
| Citations: | 39 - 5 self |
BibTeX
@INPROCEEDINGS{Yom-tov05learningto,
author = {Elad Yom-tov and Shai Fine and David Carmel and Adam Darlow},
title = {Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval},
booktitle = {In SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval},
year = {2005},
pages = {512--519},
publisher = {ACM Press}
}
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Abstract
In this article we present novel learning methods for estimating the quality of results returned by a search engine in response to a query. Estimation is based on the agreement between the top results of the full query and the top results of its sub-queries. We demonstrate the usefulness of quality estimation for several applications, among them improvement of retrieval, detecting queries for which no relevant content exists in the document collection, and distributed information retrieval. Experiments on TREC data demonstrate the robustness and the effectiveness of our learning algorithms.
