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Introduction

机译:介绍

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摘要

Argument mining (also, "argumentation mining") is a relatively new research field within the rapidly evolving area of Computational Argumentation. The tasks pursued within this field are highly challenging with many important practical applications. These include automatically identifying argumentative structures within discourse, e.g., premises, conclusion, and argumentation scheme of each argument, as well as relationships between pairs of arguments and their components. To date, researchers have investigated a plethora of methods to address these tasks in various areas, including legal documents, user generated Web discourse, on-line debates, product reviews, academic literature, newspaper articles, dialogical domains, and Wikipedia articles. Relevant manually annotated corpora are released at an increasing pace, further enhancing the research in the field. In addition, argument mining is inherently tied to sentiment analysis, since an argument frequently carries a clear sentiment towards its topic. Correspondingly, this year's workshop will be coordinated with the corresponding WASSA workshop, aiming to have a joint poster session.
机译:自变量挖掘(也称为“自变量挖掘”)是计算自变量快速发展的领域中一个相对较新的研究领域。在许多重要的实际应用中,在该领域内追求的任务具有很高的挑战性。这些包括自动识别话语中的论证结构,例如,每个论证的前提,结论和论证方案,以及成对的论证及其组成部分之间的关​​系。迄今为止,研究人员已经研究了多种方法来解决各个领域中的这些任务,包括法律文件,用户生成的Web论述,在线辩论,产品评论,学术文献,报纸文章,对话领域和Wikipedia文章。相关的手动注释语料库以越来越快的速度发布,从而进一步加强了该领域的研究。此外,自变量挖掘与情感分析本质上是联系在一起的,因为自变量经常对主题具有清晰的情感。相应地,今年的讲习班将与相应的WASSA讲习班进行协调,旨在举行联合发布会。

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