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Annotating Online Civic Discussion Threads for Argument Mining

机译:为参数挖掘注释在线公民讨论线程

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Argument mining techniques have become popular in online civic discussion thread analysis to understand an enormous amount of posts and flow of discussions for consensus building. However, the existing corpora and discussion thread analysis haven't discussed argument mining schemes sufficiently. This paper proposes a novel scheme for discussion thread analysis, annotates online civic discussions, and analyzes the annotated corpus. Our scheme consists of novel inner-and inter-post schemes. The inner-post scheme considers a post as a stand-alone discourse in a thread. We perform a micro-level annotation of argument components and relations in a post. The inter-post scheme provides a micro-level inter-post interaction to capture the argumentative reply-to relation. As a result, we have an annotated corpus including 399 threads and 5559 sentences of 204 citizens that is valid and argumentative. In addition, we analyze the annotated corpus to demonstrate statistical and linguistic properties of the corpus.
机译:自变量挖掘技术已在在线公民讨论线程分析中变得流行,以了解大量的帖子和讨论流程以建立共识。但是,现有的语料库和讨论线程分析尚未充分讨论参数挖掘方案。本文提出了一种新的讨论线程分析方案,对在线公民讨论进行注释,并分析了被注释的语料库。我们的方案由新颖的内部和内部岗位方案组成。内部帖子计划将帖子视为线程中的独立论述。我们在帖子中执行自变量成分和关系的微观注释。职位间计划提供了微观级别的职位间交互,以捕获争论性的回复关系。结果,我们得到了一个带注释的语料库,其中包括399个线程和204位公民的5559句有效且有争议的句子。此外,我们分析带注释的语料库以证明该语料库的统计和语言特性。

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