PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using RankBoost to Compare Retrieval Systems
Huyen-Trang Vu and Patrick Gallinari
In: CIKM 2005, 31 Oct - 05 Nov 2005, Germany.

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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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:689
Deposited By:Huyen-Trang Vu
Deposited On:28 November 2005