Effectiveness results for popular e-discovery algorithms

Abstract

E-Discovery applications rely upon binary text categorization to determine relevance of documents to a particular case. Although many such categorization algorithms exist, at present, vendors often deploy tools that typically include only one text categorization approach. Unlike previous studies that vary many evaluation parameters simultaneously, fail to include common current algorithms, weights, or features, or used small document collections which are no longer meaningful, we systematically evaluate binary text categorization algorithms using modern benchmark e-Discovery queries (topics) on a benchmark e-Discovery data set. We demonstrate the wide variance of performance obtained using the different parameter combinations, motivating this evaluation.