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- research-articleMay 2012
Predicting Query Performance by Query-Drift Estimation
ACM Transactions on Information Systems (TOIS), Volume 30, Issue 2Article No.: 11, Pages 1–35https://doi.org/10.1145/2180868.2180873Predicting query performance, that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. We present a novel approach to this task that is based on measuring the standard deviation of retrieval ...
- ArticleSeptember 2009
Predicting Query Performance by Query-Drift Estimation
ICTIR '09: Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval TheoryPages 305–312https://doi.org/10.1007/978-3-642-04417-5_30Predicting <em>query performance</em> , that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. Our novel approach to addressing this challenge is based on estimating the potential amount ...
- posterJuly 2008
Query-drift prevention for robust query expansion
SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrievalPages 825–826https://doi.org/10.1145/1390334.1390524Pseudo-feedback-based automatic query expansion yields effective retrieval performance on average, but results in performance inferior to that of using the original query for many information needs. We address an important cause of this robustness issue,...
- ArticleJuly 2007
Towards robust query expansion: model selection in the language modeling framework
SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrievalPages 729–730https://doi.org/10.1145/1277741.1277880We propose a language-model-based approach for addressing the performance robustness problem -- with respect to free-parameters' values -- of pseudo-feedback-based query-expansion methods. Given a query, we create a set of language models representing ...
- ArticleAugust 2005
Better than the real thing?: iterative pseudo-query processing using cluster-based language models
SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrievalPages 19–26https://doi.org/10.1145/1076034.1076041We present a novel approach to pseudo-feedback-based ad hoc retrieval that uses language models induced from both documents and clusters. First, we treat the pseudo-feedback documents produced in response to the original query as a set of pseudo-query ...