Argumentation Analysis for the Web

网络论证分析

• 批准号: 289260690
• 负责人: Professorin Dr. Iryna Gurevych
• 金额: --
• 依托单位: Fachgebiet Ubiquitäre Wissensverarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/289260690?language=en
• 关键词: Argumentation; Analysis; Web

Argumentation mining deals with the automatic identification of arguments and their relations from natural language text. This research project targets at the specific challenges of argumentation mining for the web. We seek to establish foundations of algorithms that (1) robustly apply to various forms of web argumentation, (2) efficiently leverage the scale of the web, and (3) complement argumentation mining with an argumentation analysis to effectively assess important quality dimensions.The rationale of the planned project is that people compare arguments in many decision-making situations, e.g., when buying products or when forming opinions on political controversies. Nowadays, the richest and most up-to-date argument source is the web. However, searching for arguments on the web is challenging, as dozens of web pages need to be read through in order to identify and relate the relevant arguments. State-of-the-art research on argumentation mining tackles the identification and relation of arguments within a particular domain, but it does not suffice to successfully mine argumentation on the web. The web contains numerous texts with monological argumentation (like opinionated news articles) and dialogical argumentation (like the discussions below articles) from various domains. Existing argumentation mining approaches build upon specific models from argumentation theory that do not cover this variety. The approaches rely on manually annotated samples of text, which cannot be obtained for all domains due to the scale of the web. Moreover, they disregard that, especially on the web, the quality of argumentation strongly varies with respect to several dimensions, such as clarity, coherence, or the presence of fallacies. In this project, we aim to evolve models from argumentation theory to make them comply with major forms of web argumentation. Then, we will create annotated corpora with tens of thousands of argumentative web texts from different domains. To keep the annotation effort tractable, we plan to employ distant supervision and games with a purpose. Based on the corpora, we will develop and evaluate novel algorithms that mine web argumentation and that learn patterns in it, which affect measurable quality dimensions. Domain adaptation techniques, among others, will help to cope with the variety of the web. While the size of the corpora raises the need for efficiency, it will also bring unprecedented statistical insights into web argumentation across domains.We expect to obtain new knowledge about common, good, and bad ways in which people argue on the web, thereby bridging the existing gap between theory and the practical use of argumentation. The created corpora will serve as valuable resources for other researchers, and the algorithms will be able to mine argumentation that meets specific quality constraints from a variety of web texts. We believe that leveraging such argumentation will shape the future of the web search.

论证挖掘处理从自然语言文本中自动识别论证及其关系。这个研究项目的目标是在特定的挑战，论证挖掘的网络。我们寻求建立算法的基础，（1）稳健地应用于各种形式的网络论证，（2）有效地利用网络的规模，（3）用论证分析补充论证挖掘，以有效地评估重要的质量维度。计划项目的基本原理是人们在许多决策情况下比较论证，例如，在购买产品或对政治争议形成意见时。如今，最丰富和最新的论点来源是网络。然而，在网络上搜索论点是一项挑战，因为需要通读数十个网页才能识别和联系相关论点。论证挖掘的最新研究解决了特定领域内论证的识别和关系，但它不足以成功地挖掘Web上的论证。网络上包含了许多来自不同领域的具有独白式论证（如固执己见的新闻文章）和对话式论证（如文章下面的讨论）的文本。现有的论证挖掘方法建立在不涵盖这种多样性的论证理论的特定模型之上。这些方法依赖于手动注释的文本样本，由于网络的规模，无法为所有领域获得这些样本。此外，他们忽视了，特别是在网络上，论证的质量在几个方面有很大的差异，例如清晰度，连贯性或谬误的存在。在这个项目中，我们的目标是从论证理论中发展模型，使它们符合网络论证的主要形式。然后，我们将创建带有注释的语料库，其中包含来自不同领域的数万个议论性网络文本。为了保持注释工作易于处理，我们计划采用远程监督和有目的的游戏。在语料库的基础上，我们将开发和评估新的算法，挖掘网络论证和学习模式，它会影响可衡量的质量维度。领域适应技术，除其他外，将有助于科普网络的多样性。虽然语料库的规模提高了对效率的需求，但它也将为跨领域的网络论证带来前所未有的统计见解，我们希望获得有关人们在网络上争论的常见，好的和坏的方式的新知识，从而弥合理论和论证的实际使用之间的现有差距。创建的语料库将作为其他研究人员的宝贵资源，算法将能够从各种网络文本中挖掘满足特定质量约束的论证。我们相信，利用这样的论证将塑造网络搜索的未来。

项目成果

Computational Argumentation Quality Assessment in Natural Language

• DOI: 10.18653/v1/e17-1017
• 发表时间: 2017-04
• 作者: Henning Wachsmuth;Nona Naderi;Yufang Hou;Yonatan Bilu;Vinodkumar Prabhakaran;Tim Alberdingk Thijm;Graeme Hirst;Benno Stein

Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation

在谩骂之前：网络争论中人身攻击谬误的动态和触发因素

• DOI: 10.18653/v1/n18-1036
• 发表时间: 2018
• 期刊: ArXiv
• 作者: I. Habernal;H. Wachsmuth;I. Gurevych;B. Stein
• 通讯作者: B. Stein

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

• DOI: 10.18653/v1/n18-1175
• 发表时间: 2017-08
• 期刊: ArXiv
• 作者: Ivan Habernal;Henning Wachsmuth;Iryna Gurevych;Benno Stein

SemEval-2018 Task 12: The Argument Reasoning Comprehension Task

• DOI: 10.18653/v1/s18-1121
• 发表时间: 2018-06
• 作者: Ivan Habernal;Henning Wachsmuth;Iryna Gurevych;Benno Stein

Modeling Deliberative Argumentation Strategies on Wikipedia

• DOI: 10.18653/v1/p18-1237
• 发表时间: 2018-07
• 作者: Khalid Al Khatib;Henning Wachsmuth;Kevin Lang;J. Herpel;Matthias Hagen;Benno Stein