Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity
Filip Boltužić and Jan Šnajder
2nd Workshop on Argumentation Mining (ARG-MINING 2015)
Denver, Colorado, USA, June 4, 2015
Online debates sparkle argumentative discussions from which generally accepted arguments often emerge. We consider the task of unsupervised identification of prominent argument in online debates. As a first step, in this paper we perform a cluster analysis using semantic textual similarity to detect similar arguments. We perform a preliminary cluster evaluation and error analysis based on cluster-class matching against a manually labeled dataset.
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