Quantifying test collection quality based on the consistency of relevance judgements

Relevance assessments are a key component for test collectionbased evaluation of information retrieval systems. This paper reports on a feature of such collections that is used as a form of ground truth data to allow analysis of human assessment error. A wide range of test collections are retrospectively examined to determine how accurately assessors judge the relevance of documents. Our results demonstrate a high level of inconsistency across the collections studied. The level of irregularity is shown to vary across topics, with some showing a very high level of assessment error.