Dr. Mohammad M. AL Asswad of the Legal Information Institute, and Deepthi Rajagopalan and Neha Kulkarni of Cornell University, presented a poster entitled Definitions Extractions from Code of Federal Regulations, at Cornell University’s BOOM 2014 competition.
Here is a description of the poster:
Working collaboratively with Cornell Law School students Alice Chavaillard and Rodica Turtoi, the team developed software that uses natural language processing and machine learning techniques [balanced random forest] to identify sections of federal law that define important terms. In this collaborative project, the Cornell Law students served as domain area experts and helped to produce the data needed to train the computers to classify a paragraph of text as a definition or non-definition. The engineering team then wrote software that determines the scope of the definition (where the definition applies), parses out the defined terms, and finds the boundaries of definitions that are long and complex. Once defined, the definition may be linked to other parts of relevant regulations. So when you find the term water in your particular regulation, you can click the term to be taken to the specific definition of water that applies to you, whether the definition resides in that regulation or in another section of the law.[…]