Assessor error in stratified evaluation

  • Authors:

  • William Webber;Douglas W. Oard;Falk Scholer;Bruce Hedin

  • Affiliations:

  • University of Melbourne, Melbourne, Australia;University of Maryland, College Park, MD, USA;RMIT University, Melbourne, Australia;H5, San Francisco, CA, USA

  • Venue:
  • CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
  • Year:
  • 2010

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Abstract

Several important information retrieval tasks, including those in medicine, law, and patent review, have an authoritative standard of relevance, and are concerned about retrieval completeness. During the evaluation of retrieval effectiveness in these domains, assessors make errors in applying the standard of relevance, and the impact of these errors, particularly on estimates of recall, is of crucial concern. Using data from the interactive task of the TREC Legal Track, this paper investigates how reliably the yield of relevant documents can be estimated from sampled assessments in the presence of assessor error, particularly where sampling is stratified based upon the results of participating retrieval systems. We show that assessor error is in general a greater source of inaccuracy than sampling error. A process of appeal and adjudication, such as used in the interactive task, is found to be effective at locating many assessment errors; but the process is expensive if complete, and biased if incomplete. An unbiased double-sampling method for resolving assessment error is proposed, and shown on representative data to be more efficient and accurate than appeal-based adjudication.