Caroline Privault

A new tangible user interface for machine learning document review

This paper describes a tool for assisting lawyers and paralegal teams during document review in eDiscovery. The tool combines a machine learning technology (CategoriX) and advanced multi-touch interface capable of not only addressing the usual cost, time and accuracy issues in document review, but also of facilitating the work of the review teams by capitalizing on the intelligence of the reviewers and enabling collaborative work.

Experimenting word embeddings in assisting legal review

As advanced technologies, such as data mining become part of the everyday workflow of document reviews in litigations, keyword-search still appears to serve as a cornerstone approach in responsive or privilege review. Keywords are conceptually easy to understand and help culling documents at the early stages of the review. But developing proper keywords to minimize the risk of under/over-inclusiveness can lead to complex strategies. To cope with the burden of designing search terms, we propose to use word embedding techniques in a dynamic manner. This paper describes a system leveraging semantic models in a smart review environment in order to support knowledge workers in eDiscovery.