论证挖掘的演变：从模型到社交媒体和新兴工具
The evolution of argumentation mining: From models to social media and emerging tools
摘要：
论证挖掘是计算语言学领域的一个新兴课题，主要研究从自然文本中提取结构化的论证，通常是从非结构化或噪声文本中提取。建模论证的最初方法旨在确定特定领域（法律，科学论文）的完美论证，以满足特定需求（完整性，有效性）。随着Web 2.0的出现和社交媒体的爆炸式使用，数据的传播和论点结构都发生了变化。在这篇调查文章中，我们弥合了论证挖掘的理论方法与满足社交媒体生成数据需求的实用方案之间的差距，认识到需要适应更灵活和可扩展的方案，能够适应社交媒体中存在的论证条件。我们回顾，比较和分类现有的方法，技术和工具，确定结合任务和功能的积极成果，并最终提出了一个概念架构框架。所提出的理论框架是一个论证挖掘计划，能够识别不同的子任务和捕获的社会媒体文本的需求，揭示了需要采用更灵活和可扩展的框架。
英文摘要：
Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
