We present an annotation scheme describing the argument structure of judgement documents, a central construct in Japanese law. To support the final goal of this work, namely summarisation aimed at the legal professions, we have designed blueprint models of summaries of various granularities, and our annotation model in turn is fitted around the information needed for the summaries. In this paper we report results of a manual annotation study, showing that the annotation is stable. The annotated corpus we created contains (...) 89 documents. We also designed and implemented the first two stages of an algorithm for the automatic extraction of argument structure, and present evaluation results. (shrink)

Enthymeme reconstruction, i.e. the task of reformulating arguments with missing propositions, is an exciting task at the borderline of text understanding and argument interpretation. However, there is some doubt in the community about the feasibility of this task due to the wide range of possible reformulations that are open to humans. We therefore believe that research on how to define an objective ground truth for these tasks is necessary before any work on the automatic reconstruction can begin. Here, we present (...) a feasibility study for the task of finding and expanding enthymemes involving a fortiori arguments in real-world texts, and we show that given a sufficiently strict reformulation of the human annotation task, substantial agreement can be achieved. We split the task into three sub-tasks: 1. deciding whether a candidate text span really represents an enthymematic argument, 2. classifying the type of a fortiori argument concerned and 3. describing the missing premise in natural language. In a case study involving the two authors of this paper as annotators, we test a specific type of a fortiori arguments, the let alone construction, for its suitability for reaching high agreement in all three stages of the task. We also discuss pragmatic effects of let alone and how they relate to argumentation theory. (shrink)