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 semantics, where linguistic objects are mapped by means of semantic inferences at a textual level. TE is then coupled together with an abstract bipolar argumentation system which allows to identify the arguments that are accepted in the considered online debate. Second, we address the problem of studying and comparing the different proposals put forward for modelling the support relation. The emerging scenario shows that there is not a unique interpretation of the support relation. In particular, different combinations of additional attacks among the arguments involved in a support relation are proposed. We provide an NL account of the notion of support based on online debates, by discussing and evaluating the support relation among arguments with respect to the more specific notion of TE in the NL processing field. Finally, we carry out a comparative evaluation of four proposals of additional attacks on a sample of NL arguments extracted from Debatepedia. The originality of the proposed framework lies in the following point: NL debates are analysed and the relations among the arguments are automatically extracted.