Toward constructing evidence-based legal arguments using legal decision documents and machine learning

This paper explores how to extract argumentation-relevant information automatically from a corpus of legal decision documents, and how to build new arguments using that information. For decision texts, we use the Vaccine/Injury Project (V/IP) Corpus, which contains default-logic annotations of argument structure. We supplement this with presuppositional annotations about entities, events, and relations that play important roles in argumentation, and about the level of confidence that arguments would be successful. We then propose how to integrate these semantic-pragmatic annotations with syntactic and domain-general semantic annotations, such as those generated in the DeepQA architecture, and outline how to apply machine learning and scoring techniques similar to those used in the IBM Watson system for playing the Jeopardy! question-answer game. We replace this game-playing goal, however, with the goal of learning to construct legal arguments.