Evaluation of Machine-Learning Protocols for Technology-Assisted Review in Electronic Discovery

Gordon V. Cormack & Maura R. Grossman

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Proceedings of
SIGIR 2014: The 37th Annual ACM SIGIR Conference on Research and Development in Information Retrieval

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Using a novel evaluation toolkit that simulates a human reviewer in the loop, we compare the effectiveness of three machine-learning protocols for technology-assisted review as used in document review for discovery in legal proceedings. Our comparison addresses a central question in the deployment of technology-assisted review: Should training documents be selected at random, or should they be selected using one or more non-random methods, such as keyword search or active learning? On eight review tasks -- four derived from the TREC 2009 Legal Track and four derived from actual legal matters -- recall was measured as a function of human review effort. The results show that entirely non-random training methods, in which the initial training documents are selected using a simple keyword search, and subsequent training documents are selected by active learning, require substantially and significantly less human review effort (P<0.01) to achieve any given level of recall, than passive learning, in which the machine-learning algorithm plays no role in the selection of training documents. Among passive-learning methods, significantly less human review effort (P<0.01) is required when keywords are used instead of random sampling to select the initial training documents. Among active-learning methods, continuous active learning with relevance feedback yields generally superior results to simple active learning with uncertainty sampling, while avoiding the vexing issue of "stabilization" -- determining when training is adequate, and therefore may stop.