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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683007
• 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.**

科学研究文章是由科学家撰写的，目的是为一个新的科学主张辩护，并说服其他科学家接受这个主张。哲学家图尔敏提出了一个框架，捕捉人们如何使用语言来说服他人。这个框架，从观察和分析法律的论点，并转化为实验科学竞技场，是由一个科学的主张与证据支持，从作者的科学实验。在生物医学领域，积累了太多的新科学知识，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科普这种信息洪流，将所有这些新知识聚合成可用的形式。 我的研究计划将提供一些新的计算技术。这些新技术的关键将是以计算机可操作的格式捕获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家在非计算方面进行了充分的研究：（口头和书面）语言中的修辞方法，以及科学写作中引用，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翻译成抽象辩证框架，一个国家的最先进的论证人工智能模型。为了构建论证结构，必须识别这些组成部分。这是使用标准的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。拥有基于计算机的科学论证表示可以导致生物医学研究如何进行的重大变化。可以检测不一致的实验结果，可以发现未被识别的生物医学关系，并且大量的生物医学文献将被合成为更容易获得的形式供临床医生使用。 有了这些信息，临床医生将能够更自信地诊断罕见病例，并作为其临床实践的一部分，将能够选择更合适的基因检测来确认他们的诊断。