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Apprentissage Statistique pour la Constitution de Corpus d'évaluation
Huyen-Trang Vu and Patrick Gallinari
I3 (Information - Interaction - Intelligence)
Volume 7,
Number 1,
2007.
AbstractTest collections play a crucial role for information retrieval system
evaluation. The acquisition of relevance assessment has been
recognized as the
most expensive part in test collection building, especially on very
large size document collections. This paper presents a new method
for efficiently selecting documents for the assessment set. The
method is based on machine learning algorithms which directly learn
to rank documents according to their relevance to the queries. It
leads to smaller pools than traditional round robin pooling or
alternative proposals. It thus reduces significantly the manual
assessment workload. Experimental results on TREC collections
consistently demonstrate the effectiveness of our approach according
to different evaluation criteria.
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