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Automatic ranking of retrieval models using retrievability measure

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Abstract

Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments).

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Notes

  1. Available at http://www.ir-facility.org/research/evaluation/trec-chem-09.

  2. For generating query sets, we first order all queries in \(Q\) on the basis of simplified query clarity score (SCS) [16]. Then, we extract three query subsets from \(Q\). First from low SCS range (low-quality queries), second from middle SCS range (medium-quality queries) and third from high SCS range (high-quality queries).

  3. The complete log of genetic programming and optimized retrieval models up to 100 generation is available at http://www.ifs.tuwien.ac.at/~bashir/Relationhip_Retrievability_Effectiveness.htm.

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  1. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Vienna, Austria

    Shariq Bashir & Andreas Rauber

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  1. Shariq Bashir
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Correspondence to Shariq Bashir.

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Bashir, S., Rauber, A. Automatic ranking of retrieval models using retrievability measure. Knowl Inf Syst 41, 189–221 (2014). https://doi.org/10.1007/s10115-014-0759-6

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