On the Evaluation of the Quality of Relevance Assessments Collected through Crowdsourcing

Gabriella Kazai and Natasa Milic-Frayling

Abstract

Established methods for evaluating information retrieval systems rely upon test collections that comprise document corpora, search topics, and relevance assessments. Building large test collections is, however, an expensive and increasingly challenging process. In particular, building a collection with a sufficient quantity and quality of relevance assessments is a major challenge. With the growing size of document corpora, it is inevitable that relevance assessments are increasingly incomplete, diminishing the value of the test collections. Recent initiatives aim to address this issue through crowdsourcing. Such techniques harness the problem-solving power of large groups of people who are compensated for their efforts monetarily, through community recognition, or by the entertaining experience. However, the diverse backgrounds of the assessors and the incentives of the crowdsourcing models directly influence the trustworthiness and the quality of the resulting data. Currently there are no established methods to measure the quality of the collected relevance assessments. In this paper, we discuss the components that could be used to devise such measures. Our recommendations are based on experiments with collecting relevance assessments for digitized books, conducted as part of the INEX Book Track in 2008.

Details

Publication typeInproceedings
Published inSIGIR Workshop on Future of IR Evaluation
PublisherAssociation for Computing Machinery, Inc.
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