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A Signal-to-Noise Approach to Score Normalization Avi Arampatzis Jaap Kamps
 

Summary: A Signal-to-Noise Approach to Score Normalization
Avi Arampatzis Jaap Kamps
Archives and Information Studies, Media Studies
University of Amsterdam, The Netherlands
avi.arampatzis@gmail.com kamps@uva.nl
ABSTRACT
Score normalization is indispensable in distributed retrieval and fu-
sion or meta-search where merging of result-lists is required. Dis-
tributional approaches to score normalization with reference to rel-
evance, such as binary mixture models like the normal-exponential,
suffer from lack of universality and troublesome parameter estima-
tion especially under sparse relevance. We develop a new approach
which tackles both problems by using aggregate score distributions
without reference to relevance, and is suitable for uncooperative
engines. The method is based on the assumption that scores pro-
duced by engines consist of a signal and a noise component which
can both be approximated by submitting well-defined sets of arti-
ficial queries to each engine. We evaluate in a standard distributed
retrieval testbed and show that the signal-to-noise approach yields
better results than other distributional methods. As a significant

  

Source: Arampatzis, Avi - Department of Electrical and Computer Engineering, Democritus University of Thrace

 

Collections: Computer Technologies and Information Sciences