Argumentation in scholarly biomedical literature: Computational theory, implementation, and supporting deep learning software

生物医学学术文献中的争论：计算理论、实现和支持深度学习软件

• 批准号: RGPIN-2020-06463
• 负责人: Mercer, Robert
• 金额: $ 2.99万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749868
• 关键词: Argumentation; scholarly; biomedical; literature; Computational

Scientific research articles are structured arguments meant to persuade the reader to accept the authors' research claim. My research program is (1) to develop a computational theory of argumentation structures found specifically in published biomedical research papers, and (2) to translate these text-based argumentation structures to Abstract Dialectical Frameworks (ADFs), the most general computational model of argumentation in the Nonmonotonic Reasoning sub-field of Artificial Intelligence. There are a number of novel aspects of my research program: 1) the argumentation to be identified will include arguments in single papers and across multiple papers, 2) it incorporates deep learning techniques, and 3) it uses a Big Data problem to test the robustness of ADFs. Outcomes from our text analytics will have profound societal benefits in the fields of experimental and clinical medicine. The research program has started and will continue to develop a computational theory of rhetorical moves, a precursor of argumentation structure, for biochemistry articles. Following on our successful investigation of argumentation in the Discussion sections of biochemistry journal papers, we will further investigate argumentation in Introduction, Results, and Methods sections of these articles. The culmination of this is an argumentation structure for a single paper. Tracing citations will permit the formation of arguments across multiple papers. The argumentation is rooted in the author's claim and is supported by the accompanying experimental evidence. To construct the argumentation structure, these components must be identified in the text. This has been accomplished using standard supervised machine learning (ML) methodology which requires analysis of the text using CL techniques to extract text-based features to generate the model. This methodology will be enhanced with the use of deep learning. The text analysis will use the many widely used information resources (ontologies, databases, software tools) that have been developed by biomedical and CL researchers for this purpose. We are developing novel information extraction resources to distinguish some of these text features: an ontology of verb usage in scientific writing, phenotype name recognition, and method-mention recognition, among others. A final goal of this computational approach to argumentation structure in biomedical texts is to map texts into Abstract Dialectical Frameworks. 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.

科学研究文章是旨在说服读者接受作者研究主张的结构化论点。我的研究计划是（1）开发一种在发表的生物医学研究论文中发现的论证结构的计算理论，以及（2）将这些基于文本的论证结构转化为抽象方言框架（ADFS），这是人类人工智能的非单声音量次级推理中最通用的论证计算模型。我的研究计划有许多新颖的方面：1）要确定的论证将包括在单纸和多个论文中包括参数，2）它结合了深度学习技术，3）它使用大数据问题来测试ADF的稳健性。我们的文本分析的结果将在实验和临床医学领域具有深远的社会利益。该研究计划已经启动，并将继续开发一种对生物化学文章的修辞性动作的计算理论，这是论证结构的先驱。在我们在《生物化学期刊》论文的讨论部分中对论证的成功调查之后，我们将进一步调查这些文章的引言，结果和方法部分中的论证。这是一篇论文的论证结构。追踪引用将允许在多个论文中形成论据。该论点植根于作者的主张，并得到随附的实验证据的支持。要构建论证结构，必须在文本中标识这些组件。这是使用标准监督机学习（ML）方法来完成的，该方法需要使用CL技术对文本进行分析以提取基于文本的功能以生成模型。使用深度学习将增强这种方法。文本分析将使用生物医学和CL研究人员开发的许多广泛使用的信息资源（本体论，数据库，软件工具）。我们正在开发新颖的信息提取资源，以区分以下文本特征：科学写作中动词使用的本体论，表型名称识别和方法构成识别等。这种计算方法在生物医学文本中进行论证结构的最终目标是将文本映射到抽象的方言框架中。具有基于计算机的科学论证表示可能会导致生物医​​学研究的执行方式发生重大变化。可以检测到不一致的实验结果，可以发现未识别的生物医学关系，并且将大量的生物医学文献合成为更容易获得的临床医生使用形式。