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Analyses of Multiple-Evidence Combinations for Retrieval Abdur Chowdhury, Ophir Frieder, David Grossman, Catherine McCabe
 

Summary: Analyses of Multiple-Evidence Combinations for Retrieval
Strategies
Abdur Chowdhury, Ophir Frieder, David Grossman, Catherine McCabe
Information Retrieval Lab
Illinois Institute of Technology
{abdur, ophir, dagr, catherm}@ir.iit.edu
1 Introduction
Multiple-evidence techniques are touted as means to improve
the effectiveness of systems. Belkin, et al. [1] examined the effects
of various query representations. Fox, et al. [2] proposed several
combination algorithms and found that combinations of the same
types of runs (long and short queries within the vector space
model) did not yield improvement and sometimes even degraded
performance. He did achieve improvement over individual runs
when merging different retrieval strategies (e.g., vector space and
p-norm Boolean). Lee [3] further examined various combination
algorithms for fusing result sets to improve effectiveness. He
identified that, for multiple-evidence to improve system
effectiveness, the retrieved sets must have higher relevance overlap
than non-relevance overlap. Lee did not identify the exact

  

Source: Argamon, Shlomo - Department of Computer Science, Illinois Institute of Technology

 

Collections: Computer Technologies and Information Sciences