Regulations and legislations are regularly updated, which significantly burdens up the lawyers and compliance officers with a firehose of changes. However, not all changes are significant, and only a percentage of them are of legal importance. This percentage can certainly vary in different types of regulations. This paper focuses on automatic detection or ranking of meaningful legal changes, and presents a preliminary approach based on machine learning for the same, in the domain of Internal Revenue Code (IRC) related regulatory documents. Such system would provide the users with a means to quickly identify significant legal changes.