By John Tredennick
How It Works and Why It Matters for E-Discovery
Peer-Reviewed Study Compares TAR Protocals
Two of the leading experts on e-discovery, Maura R. Grossman and Gordon V. Cormack, presented a 2014 peer-reviewed study on continuous active learning to the annual conference of the Special Interest Group on Information Retrieval, a part of the Association for Computing Machinery (ACM), “Evaluation of Machine-Learning Protocols for Technology-Assisted Review in Electronic Discovery.”
In the study, they compared three TAR protocols, testing them across eight different cases. Two of the three protocols, Simple Passive Learning (SPL) and Simple Active Learning (SAL), are typically associated with early approaches to predictive coding, which we call TAR 1.0. The third, continuous active learning (CAL), is a central part of a newer approach to predictive coding, which we call TAR 2.0.
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