Scientific Questions: A New Target for Biomedical NLP

科学问题：生物医学 NLP 的新目标

基本信息

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
项目类别：
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

项目摘要

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）技术现在已广泛（例如Google翻译），并且有几个生物医学研究中的重要应用。我们提出了一个NLP的新目标：提取科学的目标出版物中指出的问题。一种自动捕获和组织科学问题的系统在整个生物医学文献中，可能会产生广泛的重大影响，如我们的潜水收集研究人员，期刊编辑，教育工作者和科学基础的支持信。先前的工作专注于对文本是对冲还是不确定的二进制（或概率）评估在信息提取任务中降低此类语句的降低 - 不是计算捕获什么不确定性即将到来。相比之下，我们提出了一个雄心勃勃的计划，以识别，代表，整合和理性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生物医学研究中的重要新用例：将实验结果与文学结果相关化意识。上下文化结果是将基因组规模结果元素链接到打开问题的任务在所有生物医学研究中。文学意识是理解重要特征的能力整个研究出版物的大量动态集合。我们建议生产丰富的计算研究问题动态演变的表示，以及原型文本和视觉界面的原型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生物医学研究的任何领域。