A unified model for metasearch, pooling, and system evaluation
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Providing consistent and exhaustive relevance assessments for XML retrieval evaluation
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Sound and complete relevance assessment for XML retrieval
ACM Transactions on Information Systems (TOIS)
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This paper presents a new pooling method for constructing the assessment sets used in the evaluation of retrieval systems. Our proposal is based on RankBoost, a machine learning voting algorithm. It leads to smaller pools than classical pooling and thus reduces the manual assessment workload for building test collections. Experimental results obtained on an XML document collection demonstrate the effectiveness of the approach according to different evaluation criteria.