Argumentation in scholarly biomedical literature: A computational theory and its software implementation

生物医学学术文献中的论证：计算理论及其软件实现

• 批准号: RGPIN-2015-03902
• 负责人: Mercer, Robert
• 金额: $ 2.11万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659594
• 关键词: Argumentation; scholarly; biomedical; literature; computational

Scientific research articles are written by scientists to argue for a new scientific claim and to persuade other scientists to accept this claim. The philosopher Toulmin has proposed a framework that captures how people use language to convince others. This framework, drawn from observation and analysis of legal arguments and transformed into the experimental science arena, is composed of a scientific claim backed up with evidence drawn from the authors' scientific experiments. Far too much new scientific knowledge is being accumulated in the biomedical sciences for any person to keep abreast of. New computer technologies can help experimental scientists and clinicians cope with this deluge of information by aggregating all of this new knowledge into a useable form.  My research program will provide some of these new computational technologies. A key to these new technologies will be methods to capture these persuasive arguments in a format that is manipulable by computers. Argumentation is accomplished through the use of a variety of rhetorical devices, all having been well-studied non-computationally by applied linguists and rhetoricians of science: rhetorical methods in (oral and written) language, and the use of citations, figures, tables, etc. in scientific writing. My research program will capture these rhetorical devices that are found specifically in published biomedical research papers in a computational theory of argumentation structures and translate these text-based argumentation structures into Abstract Dialectical Frameworks, a state-of-the-art Artificial Intelligence model of argumentation. To construct the argumentation structure, these components must be identified. This is accomplished using standard supervised Machine Learning methodology which requires analysis of the text using Computational Linguistics techniques to extract text-based features to generate the machine learned model. The text analysis will use the many widely used information resources (ontologies, databases, software tools) that have been developed by Biomedical and Computational Linguistics researchers for this purpose. In addition we will also develop novel methods to extract information from tables. Outcomes of this research program will have profound societal benefits in the fields of experimental and clinical medicine. Having computer-based representations of scientific arguments can lead to significant changes in how biomedical research is performed. Inconsistent experimental results can be detected, unrecognized biomedical relationships can be discovered, and the vast biomedical literature will be synthesized into a more accessible form for clinicians' use. Clinicians, with this information, will be able to diagnose rare cases more confidently and as part of their clinical practice will be able to choose more appropriate genetic tests to confirm their diagnoses.**

科学家撰写科学研究文章是为了支持一种新的科学主张，并说服其他科学家接受这种说法。哲学家图尔明提出了一个框架，该框架捕捉了人们如何使用语言来说服他人。这一框架是从对法律论点的观察和分析中得出的，并转化为实验科学领域，由一个科学主张组成，并以作者的科学实验中提取的证据为依据。生物医学科学积累了太多新的科学知识，任何人都无法跟上。新的计算机技术可以将所有这些新知识聚合成一种可用的形式，从而帮助实验科学家和临床医生应对这些信息的洪流。我的研究计划将提供其中一些新的计算技术。这些新技术的一个关键将是以计算机可操纵的格式捕捉这些有说服力的论点的方法。论证是通过使用各种修辞手段来完成的，所有这些都得到了应用语言学家和科学修辞学家的非计算研究：(口头和书面)语言的修辞方法，以及在科学写作中引用、图表等的使用。我的研究项目将捕捉这些在发表的生物医学研究论文中专门找到的修辞手段，这些论文发表在论证结构的计算理论中，并将这些基于文本的论证结构翻译成抽象辩证框架，这是一种最先进的人工智能论证模型。要构建论证结构，必须确定这些组成部分。这是使用标准的有监督机器学习方法来完成的，该方法需要使用计算语言学技术来分析文本以提取基于文本的特征来生成机器学习模型。文本分析将使用生物医学和计算语言学研究人员为此目的开发的许多广泛使用的信息资源(本体论、数据库、软件工具)。此外，我们还将开发从表格中提取信息的新方法。这项研究计划的成果将在实验和临床医学领域产生深远的社会效益。以计算机为基础的科学论证表达方式可能会导致生物医学研究方式的重大变化。不一致的实验结果可以被检测到，未被识别的生物医学关系可以被发现，大量的生物医学文献将被合成成更容易获得的形式，供临床医生使用。有了这些信息，临床医生将能够更有信心地诊断罕见病例，并作为其临床实践的一部分，将能够选择更适当的基因测试来确认他们的诊断。