Ashley, Kevin and Litman, Diane and He, Daqing and Hwa, Rebecca and Anderson, James
(2021)
Center for Text Analytic Methods in Legal Studies.
In: Pitt Momentum Fund 2021.
Abstract
This Teaming Grant supports a multi-disciplinary collaboration across the School of Law, the School of Computing and Information (SCI), and the RAND Corporation to develop a Center for Text Analytic Methods in Legal Studies. The new center takes advantage of unique expertise at the University of Pittsburgh and its environs: SCI’s methodological excellence in developing and applying new tools for analyzing large text corpora and RAND’s substantive excellence in conducting impactful empirical research on legal issues of social importance. More specifically, the Pitt team members have considerable experience in developing natural language processing (NLP) and machine learning (ML) techniques and adapting them to the unique features of legal language and documents. The RAND team member has a remarkable track record of devising empirical methods to measure the social effects of laws or legal institutions such as systems for the defense of indigents.
New developments in NLP and ML and the availability of large text corpora, such as the Harvard Law School Caselaw Access Project’s data comprising 6.7 million federal and state court decisions, make it possible to analyze legal texts as never before. The new tools enable collecting data-supported evidence on the existence of entities, patterns, and relationships in the legal data, so that one can assess hypotheses about law with new kinds of empirically based arguments. The Center will focus on developing and applying the NLP/ML tools to evaluate hypotheses about systemic aspects of court decisions on social issues involving racism, gender equality, immigration, public health, crime, and education. The Center will introduce legal domain experts at RAND and Pitt Law to the possibilities for applying the new techniques and text corpora in investigating hypotheses in their specialty areas. It will explore the pedagogical potential of engaging law and pre-law students in annotating legal cases as a way to improve case reading skills and train machine learning models. By demonstrating that it can successfully apply NLP/ML methods to evaluate empirical hypotheses in law about important social issues, the Center will support subsequent funding requests via a Scaling Grant and proposals for external funding.
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