  The connections among natural language processing and argumentation theory
are becoming stronger in the latest years, with a growing amount of works going
in this direction, in different scenarios and applying heterogeneous
techniques. In this paper, we present two datasets we built to cope with the
combination of the Textual Entailment framework and bipolar abstract
argumentation. In our approach, such datasets are used to automatically
identify through a Textual Entailment system the relations among the arguments
(i.e., attack, support), and then the resulting bipolar argumentation graphs
are analyzed to compute the accepted arguments.
