Semantic Types for Computational Legal Reasoning: Propositional Connectives and Sentence Roles in the Veterans’ Claims Dataset, co-authored with Ji Hae Han, Xiang Ni, and Kaneyasu Yoseda, in the Proceedings of the 16th International Conference on Artificial Intelligence and Law (ICAIL 2017), London, UK, June 2017, pp. 217-26 (ACM: New York, 2017).
This paper announces the creation and public availability of a dataset of annotated decisions adjudicating claims by military veterans for disability compensation in the United States. This is intended to initiate a collaborative, transparent approach to semantic analysis for argument mining from legal documents. The dataset is being used in the LUIMA argument-mining project. We address two major sub-tasks for making legal reasoning computable. First, we report the semantic types of propositional connective we use to extract information about legal rules from sentences in statutes, regulations, and appellate court decisions, and to represent those rules as integrated systems. Second, we report the semantic types of sentence role we use to extract and represent the fact-finding reasoning found in adjudicatory decisions, with the goal of identifying successful and unsuccessful patterns of evidentiary argument. For each type system, we provide explanations and examples. Thus, we hope to stimulate a shared effort to create diverse datasets in law, to empirically evolve optimal sets of semantic types for argument mining, and to refine protocols for accurately applying those types to texts.