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Argumentation Mining addresses the challenging tasks of identifyingboundaries of argumentative text fragments and extracting their relationships.Fully automated solutions do not reach satisfactory accuracy due to theirinsufficient incorporation of semantics and domain knowledge. Therefore,experts currently rely on time-consuming manual annotations. In this paper, wepresent a visual analytics system that augments the manual annotation processby automatically suggesting which text fragments to annotate next. The accuracyof those suggestions is improved over time by incorporating linguisticknowledge and language modeling to learn a measure of argument similarity fromuser interactions. Based on a long-term collaboration with domain experts, weidentify and model five high-level analysis tasks. We enable close reading andnote-taking, annotation of arguments, argument reconstruction, extraction ofargument relations, and exploration of argument graphs. To avoid contextswitches, we transition between all views through seamless morphing, visuallyanchoring all text- and graph-based layers. We evaluate our system with atwo-stage expert user study based on a corpus of presidential debates. Theresults show that experts prefer our system over existing solutions due to thespeedup provided by the automatic suggestions and the tight integration betweentext and graph views.

VIANA: Visual Interactive Annotation of Argumentation