Argument mining: our story so far

In this talk I will present an overview of the emerging field of Argument Mining and then describe our contributions in that area.

In order to cut in on a debate on the web, the participants need first to evaluate the opinions of other users to detect whether they are in favor 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 formulation of the users’ opinions is needed. Our talk addresses this open issue by proposing and evaluating the use of natural language processing techniques to identify the arguments and their relations. In particular, we adopt the textual entailment approach, a generic framework for applied semantics, where linguistic objects are mapped by means of semantic inferences at a textual level. Textual entailment is then coupled together with an abstract bipolar argumentation system which allows for an identification of the arguments that are accepted in the considered online debate. We then provide a natural language 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 textual entailment in the natural language processing field. Two application scenarios are described: Debatepedia and Twitter.

I will conclude the talk discussing open issues and perspectives drawn in particular from the Dagstuhl seminar on Argument Mining that I have recently co-organized.