Argumentation Analysis for the Web

论证挖掘研究的是从自然语言文本中自动识别论点及其关系。本研究项目针对网络议论文挖掘的具体挑战。我们试图建立算法的基础，这些算法(1)稳健地应用于各种形式的Web论证，(2)有效地利用Web的规模，(3)用论证分析来补充论证挖掘，以有效地评估重要的质量维度。计划的项目的基本原理是人们在许多决策情况下比较论点，例如在购买产品时或在对政治争议形成观点时。如今，最丰富、最新的论据来源是网络。然而，在网上搜索论点是具有挑战性的，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致力于解决特定领域中论点的识别和关系，但它不足以成功地挖掘Web上的论证。该网站包含大量文本，其中包含来自不同领域的一元论论证(如自以为是的新闻文章)和对话论证(如下面的文章讨论)。现有的论证挖掘方法建立在论证理论的特定模型上，这些模型不包括这种变化。这些方法依赖于人工标注的文本样本，由于网络的规模，无法获得所有领域的文本样本。此外，他们忽视了，尤其是在网络上，论证的质量在几个方面存在很大差异，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演变模型，使其符合网络论证的主要形式。然后，我们将创建包含来自不同领域的数万个议论Web文本的标注语料库。为了使注释工作易于处理，我们计划使用远程监督和有目的的游戏。基于语料库，我们将开发和评估挖掘网络论证和在其中学习模式的新算法，这些算法影响可测量的质量维度。除其他技术外，领域适配技术将有助于应对网络的多样性。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辩论带来前所未有的统计洞察力。我们希望获得关于人们在网络上辩论的常见、好和坏方式的新知识，从而弥合论证理论和实际应用之间的现有差距。创建的语料库将成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限制的论证。我们相信，利用这样的论证将塑造网络搜索的未来。

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.