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T H E S E C O N D I N T E R N A T I O N A L C O N F E R E N C E O N T H E T H E O R Y O F I N F O R M A T I O N R E T R I E V A L ( I C T I R 2 0 0 9 ) Modeling score distributions in information retrieval
 

Summary: T H E S E C O N D I N T E R N A T I O N A L C O N F E R E N C E O N T H E T H E O R Y O F I N F O R M A T I O N R E T R I E V A L ( I C T I R 2 0 0 9 )
Modeling score distributions in information retrieval
Avi Arampatzis Stephen Robertson
Received: 10 August 2010 / Accepted: 10 August 2010 / Published online: 26 August 2010
Springer Science+Business Media, LLC 2010
Abstract We review the history of modeling score distributions, focusing on the mixture
of normal-exponential by investigating the theoretical as well as the empirical evidence
supporting its use. We discuss previously suggested conditions which valid binary mixture
models should satisfy, such as the Recall-Fallout Convexity Hypothesis, and formulate two
new hypotheses considering the component distributions, individually as well as in pairs,
under some limiting conditions of parameter values. From all the mixtures suggested in the
past, the current theoretical argument points to the two gamma as the most-likely universal
model, with the normal-exponential being a usable approximation. Beyond the theoretical
contribution, we provide new experimental evidence showing vector space or geometric
models, and BM25, as being `friendly' to the normal-exponential, and that the non-con-
vexity problem that the mixture possesses is practically not severe. Furthermore, we review
recent non-binary mixture models, speculate on graded relevance, and consider methods
such as logistic regression for score calibration.
Keywords Score distribution Normalization Distributed retrieval Fusion Filtering
1 Introduction

  

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

 

Collections: Computer Technologies and Information Sciences