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A natural language bipolar argumentation approach to support users in online debate interactions†

With the growing use of the Social Web, an increasing number of applications for exchanging opinions with other people are becoming available online. These applications are widely adopted with the consequence that the number of opinions about the debated issues increases. In order to cut in on a debate, the participants need first to evaluate the opinions of the other users to detect whether they are in favour or against the debated issue. Bipolar argumentation proposes algorithms and semantics to evaluate the set of accepted arguments, given the support and the attack relations among them. Two main problems arise. First, an automated framework to detect the relations among the arguments represented by the natural language (NL) formulation of the users’ opinions is needed. Our paper addresses this open issue by proposing and evaluating the use of NL techniques to identify the arguments and their relations. In particular, we adopt the textual entailment (TE) approach, a generic framework for applied semant...