Scientific Questions: A New Target for Biomedical NLP

项目摘要 自然语言处理(NLP)技术现在很普遍(例如Google翻译)，并且有几个 在生物医学研究中的重要应用。我们为自然语言处理提出了一个新的目标：科学的抽取 出版物中陈述的问题。一个自动捕获和组织科学问题的系统 生物医学文献可能会产生广泛的重大影响，正如我们的不同 收集来自研究人员、期刊编辑、教育工作者和科学基金会的支持信。前期工作 专注于对文本是模糊的还是不确定的进行二元(或概率)评估，目标是 在信息提取任务中将此类语句降级-而不是在计算上捕获 不确定性是关于。相比之下，我们提出了一项雄心勃勃的计划，以确定、代表、整合和推理 关于科学问题的内容，并演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：将实验结果与文献联系起来 意识。将结果与背景联系起来的任务是将基因组规模的结果的元素与开放问题联系起来 在所有生物医学研究中。文学意识是理解人的重要特征的能力 作为一个整体，收集了大量动态的研究出版物。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的公开科学问题的详细理解 在生物医学研究的任何领域。

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.