  Protecting privileged communications and data from disclosure is paramount
for legal teams. Legal advice, such as attorney-client communications or
litigation strategy are typically exempt from disclosure in litigations or
regulatory events and are vital to the attorney-client relationship. To protect
this information from disclosure, companies and outside counsel often review
vast amounts of documents to determine those that contain privileged material.
This process is extremely costly and time consuming. As data volumes increase,
legal counsel normally employs methods to reduce the number of documents
requiring review while balancing the need to ensure the protection of
privileged information. Keyword searching is relied upon as a method to target
privileged information and reduce document review populations. Keyword searches
are effective at casting a wide net but often return overly inclusive results -
most of which do not contain privileged information. To overcome the weaknesses
of keyword searching, legal teams increasingly are using machine learning
techniques to target privileged information. In these studies, classic text
classification techniques are applied to build classification models to
identify privileged documents. In this paper, the authors propose a different
method by applying machine learning / convolutional neural network techniques
(CNN) to identify privileged documents. Our proposed method combines keyword
searching with CNN. For each keyword term, a CNN model is created using the
context of the occurrences of the keyword. In addition, a method was proposed
to select reliable privileged (positive) training keyword occurrences from
labeled positive training documents. Extensive experiments were conducted, and
the results show that the proposed methods can significantly reduce false
positives while still capturing most of the true positives.
