Detecting Bipolar Semantic Relations among Natural Language Arguments with Textual Entailment: a Study.

In the knowledge representation and rea-soning research area, argumentation the-ory aims at representing and reasoning over information items called arguments. In everyday life, arguments are reasons to believe and reasons to act, and they are usually expressed in natural language. Even if ad-hoc natural language examples are often provided in argumentation theory works, no automated processing of such natural language arguments is carried out, making it impossible to exploit the results of this research area in real world scenar-ios. In this paper, we propose to adopt tex-tual entailment to address this issue. In particular, we discuss and evaluate, on a sample of natural language arguments ex-tracted from Debatepedia, the support and attack relations among arguments in bipo-lar abstract argumentation with respect to the more specific notions of textual entail-ment and contradiction.