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.**

科学研究论文是由科学家为论证一个新的科学主张并说服其他科学家接受这一主张而写的。哲学家图尔敏（Toulmin）提出了一个框架，该框架捕捉了人们如何使用语言说服他人。这个框架是从对法律论据的观察和分析中得出的，并转化为实验科学领域，它由一个科学主张组成，并得到作者科学实验证据的支持。在生物医学科学中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帮助实验科学家和临床医生通过将所有这些新知识聚合成可用的形式来处理这些海量的信息。我的研究项目将提供一些新的计算技术。这些新技术的关键将是用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，所有这些都已经被应用语言学家和科学修辞学家进行了非计算性的研究：口头和书面语言的修辞方法，以及在科学写作中引用、数字、表格等的使用。我的研究计划将捕捉这些修辞手段，特别是在已发表的生物医学研究论文中发现的论证结构的计算理论，并将这些基于文本的论证结构转化为抽象辩证框架，这是一种最先进的人工智能论证模型。为了构造论证结构，必须识别这些组件。这是使用标准的监督机器学习方法完成的，该方法需要使用计算语言学技术对文本进行分析，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，数据库，软件工具）。此外，我们还将开发从表中提取信息的新方法。本研究项目的成果将在实验和临床医学领域产生深远的社会效益。拥有基于计算机的科学论证表示可以导致生物医学研究的执行方式发生重大变化。可以发现不一致的实验结果，可以发现未被识别的生物医学关系，并且可以将大量生物医学文献合成为更易于获取的形式供临床医生使用。有了这些信息，临床医生将能够更自信地诊断罕见病例，并且作为临床实践的一部分，他们将能够选择更合适的基因检测来确认他们的诊断