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Argument mining based on a structured database and its usage in an intelligent tutoring environment

机译：基于结构化数据库的参数挖掘及其在智能补习环境中的用法

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Argumentation theory is a new research area that concerns mainly with reaching a mutually acceptable conclusion using logical reasoning. Argumentation can be defined as a proof of dynamic nature and is considered as an ill-defined domain that typically lacks clear distinctions between “right” and “wrong” answers. Instead, there are often competing reasonable answers. Recently, a number of argument mapping tools have been developed to diagram, articulate, and comprehend different arguments. Despite the fact, these methods are of complementary nature, and the efforts for integrating these tools are missing. The purpose of this paper is threefold: (1) revealing a novel approach for argument representation using a structured relational argument database “RADB”, which has been designed, developed, and implemented in order to represent different argument analyses and diagrams, (2) presenting a classifier agent that utilizes the RADB repository by using different mining techniques in order to retrieve the most relevant arguments to the subject of search, and (3) proposing an agent-based educational environment (ALES) that utilizes the RABD together with the classifier agent to teach argument analysis.
机译：论证理论是一个新的研究领域，主要涉及使用逻辑推理得出一个可以接受的结论。争论可以被定义为动态性质的证明，并且被认为是定义不明确的领域，通常在“正确”和“错误”答案之间缺乏清晰的区分。相反，通常会有一些竞争性的合理答案。最近，已经开发了许多参数映射工具来图解，表达和理解不同的参数。尽管事实如此，但是这些方法具有互补性，并且缺少集成这些工具的工作。本文的目的是三方面的：（1）揭示一种使用结构化关系参数数据库“ RADB”进行参数表示的新颖方法，该数据库已被设计，开发和实现以表示不同的参数分析和图表，（2）展示通过使用不同的挖掘技术来利用RADB存储库的分类器代理，以检索与搜索主题最相关的参数，以及（3）提出将RABD与分类器一起使用的基于代理的教育环境（ALES）代理教论证分析。

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