Using RankBoost to Compare Retrieval systems

Huyen-Trang Vu 1 Patrick Gallinari 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : 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.
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Submitted on : Wednesday, March 15, 2017 - 2:32:31 PM
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Huyen-Trang Vu, Patrick Gallinari. Using RankBoost to Compare Retrieval systems. CIKM 2005 - 14th ACM international conference on Information and knowledge management, Oct 2005, Bremen, Germany. pp.309-310, ⟨10.1145/1099554.1099641⟩. ⟨hal-01490516⟩



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