Automating Document Review

Law firms engaged in litigation expend significant time and resources on document review, a process requiring brief examination of thousands to hundreds of thousands of client documents. Often performed by first-year associates, the document review task is essentially a classification problem: documents need to be sorted into a number of categories, based on their content, their author(s), and a variety of other features. Due to the high volume of documents to be examined, and the often simple rules needed to classify documents, this process is ripe for assistance by a natural language processing system. This paper explores the desired features of an NLP document review system and demonstrates some results using a prototype. 1 Discovery and Document Review In large litigation cases or government investigations, law firms expend significant effort in the discovery process, during which litigants must produce internal documents relevant to the matter in question, and make them available either to the opposing counsel or in response to a government subpoena. The documents in question may include memoranda, financial statements, internal work papers, and a great deal of email. The widespread use of email by employees of large corporations has significantly increased the volume of documents that need to be reviewed as part of the discovery process. For the purposes of this exploration, we will focus on the task of classifying emails; many of the other documents in question are included as attachments to emails, and references to these documents in the email body may be used for classifying both types of documents.