Computing and Information Systems - Theses
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ItemMeasurement in information retrieval evaluation( 2010)Full-text retrieval systems employ heuristics to match documents to user queries. Retrieval correctness cannot, therefore, be formally proven, but must be evaluated through human assessment. To make evaluation automatable and repeatable, assessments of which documents are relevant to which queries are collected in advance, to form a test collection. Collection-based evaluation has been the standard in retrieval experiments for half a century, but only recently have its statistical foundations been considered. This thesis makes several contributions to the reliable and efficient measurement of the behaviour and effectiveness of information retrieval systems. First, the high variability in query difficulty makes effectiveness scores difficult to interpret, analyze, and compare. We therefore propose the standardization of scores, based on the observed results of a set of reference systems for each query. We demonstrate that standardization controls variability and enhances comparability. Second, while testing evaluation results for statistical significance has been established as standard practice, the importance of ensuring that significance can be reliably achieved for a meaningful improvement (the power of the test) is poorly understood. We introduce the use of statistical power analysis to the field of retrieval evaluation, finding that most test collections cannot reliably detect incremental improvements in performance. We also demonstrate the pitfalls in predicting score standard deviation during design-phase power analysis, and offer some pragmatic methodological suggestions. Third, in constructing a test collection, it is not feasible to assess every document for relevance to every query. The practice instead is to run a set of systems against the collection, and pool their top results for assessment. Pooling is potentially biased against systems which are neither included in nor similar to the pooled set. We propose a robust, empirical method for estimating the degree of pooling bias, through performing a leave-one-out experiment on fully pooled systems and adjusting unpooled scores accordingly. Fourth, there are many circumstances in which one wishes directly to compare the document rankings produced by different retrieval systems, independent of their effectiveness. These rankings are top-weighted, non-conjoint, and of arbitrary length, and no suitable similarity measures have been described for such rankings. We propose and analyze such a rank similarity measure, called rank-biased overlap, and demonstrate its utility, on real and simulated data. Finally, we conclude the thesis with an examination of the state and function of retrieval evaluation. A survey of published results shows that there has been no measurable improvement in retrieval effectiveness over the past decade. This lack of progress has been obscured by the general use of uncompetitive baselines in published experiments, producing the appearance of substantial and statistically significant improvements for new systems without actually advancing the state of the art.