Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity

Proceedings of the 2nd Workshop on Argumentation Mining, pages 110–115, Denver, Colorado, June 4, 2015. c⃝2015 Association for Computational Linguistics Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity Filip Boltuˇzi´c and Jan ˇSnajder University of Zagreb, Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Unska 3, 10000 Zagreb, Croatia {filip.boltuzic,jan.snajder}@fer.hr Abstract Online debates sparkle argumentative discus- sions from which generally accepted argu- ments often emerge. We consider the task of unsupervised identification of prominent argu- ment in online debates. As a first step, in this paper we perform a cluster analysis using se- mantic textual similarity to detect similar argu- ments. We perform a preliminary cluster evalu- ation and error analysis based on cluster-class matching against a manually labeled dataset. 1 Introduction Argumentation mining aims to detect the argumenta- tive discourse structure in text. It is an emerging field in the intersection of natural language processing, logic-based reasoning, and argumentation theory; see (Moens, 2014) for a recent overview. While most work on argumentation mining has focused on well-structured (e.g., legal) text, recently attention has also turned to user-generated content such as online debates and product reviews. The main motivation is to move beyond simple opinion mining and discover the reasons underlying opinions. As users’ comments are generally less well-structured and noisy, argumentation mining proper (extraction of argumentative structures) is rather difficult. How- ever, what seems to be a sensible first step is to iden- tify the arguments (also referred to as reasons and claims) expressed by users to back up their opinions. In this work we focus on online debates. Given a certain topic, a number of prominent arguments often emerge in the debate, and the majority of users will back up their stance by one or more of these argu- ments. The problem, however, is that linking users’ statements to arguments is far from trivial. Besides language variability, due to which the same argu- ment can be expressed in infinitely many ways, many other factors add to the variability, such as entail- ment, implicit premises, value judgments, etc. This is aggravated by the fact that most users express their arguments in rather confusing and poorly worded manner. Another principal problem is that, in gen- eral, the prominent arguments for a given topic are not known in advance. Thus, to identify the argu- ments expressed by the users, one first needs to come up with a set of prominent arguments. Manual analy- sis of the possible arguments does not generalize to unseen topic nor does it scale to large datasets. In this paper, we are concerned with automatically identifying prominent arguments in online debates. This is a formidable task, but as a first step towards this goal, we present a cluster analysis of users’ ar- gumentative statements from online debates. The underlying assumption is that statements that express the same argument will be semantically more similar than statements that express different arguments, so that we can group together similar statements into clusters that correspond …

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