Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking robustness: a novel framework to predict query performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Introduction to Information Retrieval
Introduction to Information Retrieval
The Combination and Evaluation of Query Performance Prediction Methods
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Addressing morphological variation in alphabetic languages
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Reducing the risk of query expansion via robust constrained optimization
Proceedings of the 18th ACM conference on Information and knowledge management
CLEF 2009: Grid@CLEF pilot track overview
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Automated component-level evaluation: present and future
CLEF'10 Proceedings of the 2010 international conference on Multilingual and multimodal information access evaluation: cross-language evaluation forum
Frequentist and bayesian approach to information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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This article describes a large-scale empirical evaluation across different types of English text collections. We ran about 140,000 experiments and analyzed the results on system component-level to find out if we can select configurations that perform reliable on specific types of corpora. To our own surprise we observed that a specific set of configuration parameters achieved 95% of the optimal average MAP across all collections. We conclude that this configuration could be used as baseline reference for evaluation of new IR approaches on English text corpora.