Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity
نویسندگان
چکیده
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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