Semantic Types for Decomposing Evidence Assessment in Decisions on Veterans’ Disability Claims for PTSD, co-authored with Ashtyn Hemendinger, Nneka Okpara, and Tauseef Ahmed, peer-reviewed and accepted for presentation at the Second Workshop on Automated Detection, Extraction and Analysis of Semantic Information in Legal Texts (ASAIL 2017), as part of the 16th International Conference on Artificial Intelligence and Law (ICAIL 2017), London, UK, June 2017.
This paper presents a semantic analysis for mining arguments or reasoning from the evidence assessment portions (fact-finding portions) of adjudicatory decisions in law. Specifically, we first decompose the reasoning into primary branches, using a rule tree of the substantive issues to be decided. Within each branch, we further decompose argumentation using two main categories: reasoning that deploys special legal rules and reasoning that does not. With respect to special legal rules, we discuss legal-presumption rules, sufficiency-of-evidence rules, and the benefit-of-the-doubt rule. Semantic anchors for this decomposition are provided by identifying the inferential roles of sentences – principally evidence sentences, finding-of-fact sentences, evidence-based-reasoning sentences, and legal-rule sentences. We illustrate our methodology throughout the paper, using data and examples from a dataset of veterans’ disability claims in the U.S. for posttraumatic stress disorder (PTSD).