Scientific Questions: A New Target for Biomedical NLP

科学问题：生物医学 NLP 的新目标

• 批准号: 10454968
• 负责人: LAWRENCE E HUNTER
• 金额: $ 44.52万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10454968
• 关键词: Address; Area; Artificial Intelligence; Awareness; Biomedical Research; Characteristics; Collection; Computerized Patient Records; Cues; Data; Elements; Environment; Evolution; Expert Systems; Foundations; Genes; Goals; Gold; Information Retrieval; Journals; Letters; Link; Literature; Manuals; Methods; Molecular; Natural Language Processing; Ontology; Pathway Analysis; Pathway interactions; Performance; Phenotype; Proteomics; Publications; Publishing; Research; Research Personnel; Resolution; Resources; Role; Science; Scientist; Semantics; Services; Signal Transduction; Source; Students; System; Taxonomy; Technology; Text; Time; Translating; Uncertainty; Update; Visual; Work; design; dynamical evolution; experimental study; genome wide association study; genome-wide; graduate student; high throughput screening; innovation; journal article; news; novel; pharmacovigilance; prototype; symposium; text searching; tool; transcriptome sequencing; trend

Project Summary Natural language processing (NLP) technology is now widespread (e.g. Google Translate) and has several important applications in biomedical research. We propose a new target for NLP: extraction of scientific questions stated in publications. A system that automatically captures and organizes scientific questions from across the biomedical literature could have a wide range of significant impacts, as attested to in our diverse collection of support letters from researchers, journal editors, educators and scientific foundations. Prior work focused on making binary (or probabilistic) assessments of whether a text is hedged or uncertain, with the goal of downgrading such statements in information extraction tasks—not computationally capturing what the uncertainty is about. In contrast, we propose an ambitious plan to identify, represent, integrate and reason about the content of scientific questions, and to demonstrate how this approach can be used to address two important new use cases in biomedical research: contextualizing experimental results and enhancing literature awareness. Contextualizing results is the task of linking elements of genome-scale results to open questions across all of biomedical research. Literature awareness is the ability to understand important characteristics of large, dynamic collections of research publications as a whole. We propose to produce rich computational representations of the dynamic evolution of research questions, and to prototype textual and visual interfaces to help students and researchers explore and develop a detailed understanding of key open scientific questions in any area of biomedical research.

项目摘要 自然语言处理（NLP）技术现在很普遍（例如，谷歌翻译），并且具有几个 在生物医学研究中的重要应用。我们为NLP提出了一个新的目标：提取科学知识。 出版物中提到的问题。一个自动捕获和组织科学问题的系统， 在生物医学文献中，这种方法可能会产生广泛的重大影响，正如我们在各种各样的研究中所证明的那样， 收集研究人员、期刊编辑、教育工作者和科学基金会的支持信。先前工作 专注于对文本是否模糊或不确定进行二元（或概率）评估，目标是 在信息提取任务中降级这样的语句-而不是通过计算捕获 不确定性是关于。与此相反，我们提出了一个雄心勃勃的计划，以确定，代表，整合和推理 关于科学问题的内容，并展示如何使用这种方法来解决两个问题 生物医学研究中的重要新用例：将实验结果置于情境中并增强文献 意识情境化结果是将基因组规模结果的要素与开放性问题联系起来的任务 在所有的生物医学研究中。文学意识是理解能力的重要特征 作为一个整体的研究出版物的大型动态收藏。我们建议产生丰富的计算 研究问题的动态演变的表示，以及原型文本和视觉界面 帮助学生和研究人员探索和发展对关键开放科学问题的详细理解 在任何生物医学研究领域。