Strange Loop

Improving law interpretability using NLP

The process of legal reasoning is heavily reliant on information stored in text, but while legal texts are generally easily accessible, their interpretation often isn't straight forward, making the understanding of the law effectively inaccessible to the general public.

Data Scientists from Bardess, in collaboration with a research group from the Government of Ontario, have investigated how Natural Language Processing techniques can be applied to understand linguistic patterns in legislative texts and extract information that is meaningful for the public.

Using the Accessibility for Ontarians with Disabilities Act (AODA) as a test case, we developed a multi-stage analysis that combines some well known NLP methodologies in a unique approach. Ultimately we were able to automate the extraction of rules from the Act and its Regulation, identify the entities responsible for compliance, and organize them into groups that are homogeneous with respect to their impact on various entities and industries.

The methodology developed provides us with a framework for representing legal texts that can be used to simplify the way information in the law is accessed by the public and at the same time highlights parts of the law that are particularly hard to interpret and should be re-written more clearly.

Serena Peruzzo

Bardess Group

Serena is a senior data scientist at the analytics consultancy Bardess, currently based in Toronto, Canada. Before joining Bardess, she has worked both in academia as an ML researcher and in the industry as a data science consultant on the Australian, British and Canadian markets. Serena is passionate about education, community and tech for good and she splits her free time between mentoring data science students, organizing meetups and volunteering.