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Harnessing rhetorical figures for argument mining

机译:利用修辞手法进行论证和挖掘

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The generalised, automated reconstruction of the reasoning structures underlying persuasive communication is an enormously challenging task. While this work in argument mining is increasingly informed by the rich tradition of argumentation studies outside the computational field, the rhetorical perspective on argumentation is thus far largely ignored. To explore the application of rhetorical insights in argument mining, we conduct a pilot study on the connection between rhetorical figures and argumentation structure. Rhetorical figures are linguistic devices that perform a variety of functions in argumentative discourse. The textual form of some of these figures is easy to identify automatically, such that an established connection between the figure and a preponderance of argumentative content would improve the performance of argument mining techniques. Furthermore, the automated mining of rhetorical figures could be used as an empirical, corpus-based testing ground for the claims made about these figures in the rhetorical literature. In the pilot study, we explore the connection between eight rhetorical figures the forms of which we expect to be relatively easy to identify computationally, and argumentation structure (concretely, we consider the six schemes ‘anadiplosis’, ‘epanaphora’, ‘epistrophe’, ‘epizeuxis’, ‘eutrepismus’, and ‘polyptoton’, and the two tropes ‘antithesis’ and ‘dirimens copulatio’, and relate their occurrences to relations of inference and conflict). The data of the study is collected in the MM2012c corpus of 39,694 words of argumentatively annotated transcripts from the BBC Radio?4’s Moral Maze discussion program. We show that some of the figures indeed correspond to passages of high argumentative density, relative to the text as a whole.
机译:具有说服力的交流基础的推理结构的普遍,自动化的重构是一项巨大的挑战。尽管在计算领域之外,对论证挖掘的这项工作越来越多地受益于对论证研究的丰富传统,但迄今为止,关于论证的修辞学观点却被大大地忽略了。为了探讨修辞学见解在论据挖掘中的应用,我们对修辞人物与论证结构之间的联系进行了初步研究。修辞格是在论证性话语中执行多种功能的语言工具。其中一些图形的文本形式易于自动识别,因此,图形与大量议论性内容之间的已建立联系将改善议论文挖掘技术的性能。此外,对修辞格的自动挖掘可以用作修辞文献中关于这些格言的主张的基于经验的基于语料库的测试平台。在试点研究中,我们探索了八个修辞人物之间的联系,这些修辞人物的形式我们希望可以相对容易地进行计算识别,并建立论证结构(具体来说,我们考虑了六种方案“拟婚”,“透视”,“表扬”, “ epizeuxis”,“ eutrepismus”和“ polyptoton”,以及两个对立的“对立”和“ dirimens copulatio”,并将它们的出现与推论和冲突的关系联系起来。该研究的数据收集自MM2012c语料库,该语料库来自BBC Radio?4的Moral Maze讨论计划,带有39694个单词的经过论证的成绩单。我们表明,相对于整个文本,某些数字确实对应于高议论密度的段落。

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