Research into what TAR methods work better causes stir in e-discovery
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.
Based on their testing, Grossman and Cormack concluded that CAL demonstrated superior performance over SPL and SAL, while avoiding certain other problems associated with these traditional TAR 1.0 protocols. Specifically, in each of the eight case studies, CAL reached higher levels of recall (finding relevant documents) more quickly and with less effort that the TAR 1.0 protocols. Not surprisingly, their research caused quite a stir in the TAR community.
By Guest Blogger: Catalyst Repository Systems, Inc.
