Big data, little decisions: Tightening the loop between data crunching and human expertise

Abstract

This presentation is a case study examining how LexisNexis uses scaled active learning on the HPCC Systems environment to focus manual topical annotations on critical documents pulled from a large corpus. The active learning system uses natural language processing and machine learning techniques to identify and present “next best” training set candidates to legal editors, combining massive parallel processing with expert human analysis to improve classifier accuracy while minimizing human effort.