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

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Abstract

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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