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

Elena Cabrio 1 Serena Villata 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In the knowledge representation and reasoning research area, argumentation theory 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 scenarios. In this paper, we propose to adopt textual entailment to address this issue. In particular, we discuss and evaluate, on a sample of natural language arguments extracted from Debatepedia, the support and attack relations among arguments in bipo- lar abstract argumentation with respect to the more specific notions of textual entailment and contradiction.
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Elena Cabrio, Serena Villata. Detecting Bipolar Semantic Relations among Natural Language Arguments with Textual Entailment: a Study. Joint Symposium on Semantic Processing (JSSP-2013), Nov 2013, Trento, Italy. ⟨hal-00915879⟩

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