权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Argument Mining

论据挖掘

基本信息

批准号：
EP/N014871/1
负责人：
Chris Reed
金额：
$ 86.66万
依托单位：
University of Dundee
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN014871%2F1
关键词：
Argument Mining

项目摘要

Argument and debate form cornerstones of civilised society and of intellectual life. Processes of argumentation run our governments, structure scientific endeavour and frame religious belief. Recognising and understanding argument are central to decision-making and professional activity in all walks of life, which is why we place them at the centre of academic pedagogy and practice; it's why such a premium is placed upon these skills; and it's why rationality is one of the very defining notions of what it is to be human. Our theories of how argument is structured go back to Ancient Greece. In the past thirty years or so, the computational sciences have started to build models and engineer software based on these theories: this is the field of argument technology, and the recent surge in activity is testament to the vitality and broad applicability of the field. Though argument technology, in which the UK is a world leader, has had applications in domains as diverse as healthcare, public policy, government and the media, the focus has been squarely upon Artificial Intelligence technologies for supporting human argumentation and subsequent automated reasoning with the results. Arguments made outside such software walled gardens have been off the agenda simply because automatic machine understanding of unfettered naturally occurring reasoning has been too hard to tackle. Before 2014, that task -- argument mining -- had been tackled only speculatively and only in specific domains by a very small number of groups such as those at Toronto, Leuven and Dundee. By the end of 2014, more than twenty research labs across the US and EU were gearing up to tackle the problem, there were several international meetings including a regular workshop series at the largest computational linguistics conference, and dozens of results being reported. The reason for this huge upswing in activity lies in maturing technology and the returns available. Opinion mining has transformed the way that market research and PR is carried out, deploying big data analysis techniques to understand the attitudes people hold towards products and brands. Sentiment analysis has had an even greater impact in predicting financial markets by analysing broad moods and perspectives that are expressed in the press. Argument mining is the natural evolution of these technologies, providing a step change in the level of detail available -- moving from not just analysing what opinions people hold, but why they hold the opinions they do. This is why major organisations such as IBM, with whom we are partnering in this project, are so interested in the technology. The Centre for Argument Technology now curates the largest publicly accessible corpus of analysed argument in the world, and has a well known and widely used tool stack for managing datasets, conducting analyses, and visualising the results. This provides a unique platform from which we can both extend existing techniques for argument mining, but also, much more ambitiously, use insights from the philosophy of argumentation and from rhetoric to transform the reliability and applicability of argument mining technology. In particular, we will use the theory of argumentation schemes that characterises stereotypical patterns of reasoning to guide the process of searching for argument components, and the theory of rhetorical figures and tropes as the basis for developing a new class of algorithms for argument recognition. We will thus be transforming bare statistically-driven approaches with detailed theories of structure which can act to define expectations in a way that constrains the machine learning task thereby improving accuracy and applicability. By partnering with IBM and J&L Techology (a domain-specific SME), the project aims not just to radically improve performance of these techniques, establishing the UK's position at the cutting edge, but also to deliver those performance gains to end users.

争论和辩论是文明社会和知识生活的基石。论证的过程运行着我们的政府，构建科学努力，构建宗教信仰。认识和理解论点是决策和各行各业专业活动的核心，这就是为什么我们把它们放在学术教学和实践的中心;这就是为什么这些技能受到如此重视;这就是为什么理性是人类的定义概念之一。我们关于论证结构的理论可以追溯到古希腊。在过去的三十年左右，计算科学已经开始建立模型和工程软件的基础上，这些理论：这是领域的论证技术，最近的活动激增证明了生命力和广泛的适用性领域。虽然英国是世界领先的论证技术已经在医疗保健，公共政策，政府和媒体等领域得到了应用，但重点一直是人工智能技术，用于支持人类论证和随后的自动推理结果。在这种软件围墙花园之外进行的争论已经被排除在议程之外，仅仅是因为机器对不受约束的自然发生的推理的自动理解太难解决了。在2014年之前，这项任务--论点挖掘--只被推测性地处理，而且只在特定领域由极少数团体处理，如多伦多、鲁汶和邓迪的团体。到2014年底，美国和欧盟的20多个研究实验室正在准备解决这个问题，有几个国际会议，包括最大的计算语言学会议的定期研讨会系列，并报告了数十个结果。这种活动大幅上升的原因在于成熟的技术和可获得的回报。意见挖掘已经改变了市场研究和公关的方式，部署了大数据分析技术来了解人们对产品和品牌的态度。情绪分析通过分析媒体表达的广泛情绪和观点，在预测金融市场方面产生了更大的影响。论点挖掘是这些技术的自然演变，在可用的细节水平上提供了一个台阶式的变化--不仅仅是分析人们持有什么观点，而是分析他们为什么持有这些观点。这就是为什么像IBM这样的主要组织，我们在这个项目中与他们合作，对这项技术如此感兴趣。论证技术中心现在管理着世界上最大的可公开访问的分析论证语料库，并拥有一个众所周知且广泛使用的工具堆栈，用于管理数据集，进行分析和可视化结果。这提供了一个独特的平台，我们既可以扩展现有的论证挖掘技术，也可以更雄心勃勃地利用论证哲学和修辞学的见解来改变论证挖掘技术的可靠性和适用性。特别是，我们将使用的理论论证方案的特点定型模式的推理，以指导搜索过程中的参数组件，和理论的修辞数字和比喻的基础上开发一类新的算法的参数识别。因此，我们将用详细的结构理论来改造纯粹的人工智能驱动的方法，这些理论可以以一种约束机器学习任务的方式来定义期望，从而提高准确性和适用性。通过与IBM和J&L Techology（一家特定领域的中小型企业）合作，该项目的目标不仅是从根本上提高这些技术的性能，建立英国的前沿地位，而且还将这些性能收益提供给最终用户。