Legal Text Analytics and its Role in AI and Law

Traditionally, the field of AI and Law has focused on representing legal knowledge in ways that computers can use to perform legal reasoning, or something like it, with legally intelligible results. Today, the research paradigm in AI and Law has largely shifted to applying new machine learning (ML) and natural language processing techniques to legal texts. These text analytic techniques use statistical means to extract semantic information from archives of legal case decisions, contracts, or statutes. Although for some time ML models have been predicting outcomes of cases directly from textual descriptions of the facts, they cannot yet explain their predictions or support them with arguments.

This talk will briefly explain some basic mechanisms of text analytics and their application in legal practice and describe some recent research efforts to tease additional elements of meaning from legal text, including concepts and argument structures. These methods can improve legal information retrieval, may eventually enable ML models to explain and justify their results, and contribute new methods in empirical legal research.

Speaker(s): Kevin D. Ashley,

Virtual: https://events.vtools.ieee.org/m/308473