RAPID: Augmented Intelligence for Accelerating Covid-Related Scientific Discovery

RAPID：增强智能加速新冠相关科学发现

• 批准号: 2040196
• 负责人: Daniel Weld
• 金额: $ 20万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040196&HistoricalAwards=false
• 关键词: RAPID; Augmented; Intelligence; Accelerating; Covid

The project will develop new artificial intelligence (AI) methods to augment the productivity of biomedical researchers and accelerate scientific discovery in the context of the COVID-19 pandemic. We will issue weekly updates to our widely-used Cord-19 and SciSight resources, which are a critical resource for researchers studying SARS-CoV-2, having already been downloaded over 100,000 times by other researchers. We will also extend these resources to make them more useful to doctors and researchers in two ways. First, we will automatically generate one-sentence summaries of each paper to speed sensemaking of the rapidly changing literature. Second, we will automatically extract a broad range of entities (such as disease symptoms and research challenges) and relations to improve filtering and search. In order to generate one-sentence summaries of research papers, we will train an abstractive BART model, using two novel techniques: 1) co-training on the auxiliary task of title prediction, and 2) fine-tuning using a set of one-sentence summaries that we will generate by crowd-sourcing edits peer-review comments taken from sites such as OpenReview. We will test our one-sentence summary generation with a combination of automated (Rouge) metrics and user preference. In order to increase the number of entities and relations extracted from research papers, we will bootstrap with data-programming techniques then apply graph-neural-network methods. We will evaluate our progress using a combination of expert-annotated data and held out information from relevant knowledge bases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将开发新的人工智能（AI）方法，以提高生物医学研究人员的生产力，并在COVID-19大流行的背景下加速科学发现。我们将每周更新我们广泛使用的Cord-19和SciSight资源，这些资源是研究SARS-CoV-2的研究人员的关键资源，已被其他研究人员下载超过10万次。我们还将扩展这些资源，使它们在两个方面对医生和研究人员更有用。首先，我们将自动生成每篇论文的一句话摘要，以加快快速变化的文献的意义。其次，我们将自动提取范围广泛的实体（如疾病症状和研究挑战）和关系，以改进过滤和搜索。为了生成研究论文的一句话摘要，我们将使用两种新技术来训练一个抽象的BART模型：1)在标题预测的辅助任务上进行共同训练，以及2)使用一组一句话摘要进行微调，这些摘要将通过众包编辑从OpenReview等网站上获取的同行评审评论来生成。我们将使用自动化（Rouge）度量和用户偏好的组合来测试我们的一句话摘要生成。为了增加从研究论文中提取的实体和关系的数量，我们将使用数据编程技术进行引导，然后应用图神经网络方法。我们将结合专家注释的数据和来自相关知识库的信息来评估我们的进展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts

ACCoRD：生成科学概念多样化描述的多文档方法

• 发表时间: 2022
• 期刊: 2022 Conference on Empirical Methods in Natural Language Processing
• 作者: Sonia K. Murthy;Kyle Lo;Daniel King;Chandra Bhagavatula;Bailey Kuehl;Sophie Johnson;Jon Borchardt;Daniel S. Weld;Tom Hope;Doug Downey
• 通讯作者: Doug Downey

Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

• DOI: 10.1145/3491102.3501905
• 发表时间: 2021-08
• 期刊: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
• 作者: Jason Portenoy;Marissa Radensky;Jevin D. West;E. Horvitz;Daniel S. Weld;Tom Hope

A Computational Inflection for Scientific Discovery

• DOI: 10.1145/3576896
• 发表时间: 2023-08-01
• 期刊: COMMUNICATIONS OF THE ACM
• 影响因子: 22.7
• 作者: Hope, Tom;Downey, Doug;Horvitz, Eric
• 通讯作者: Horvitz, Eric

SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts

SciCo：科学概念的分层跨文档参考

• 发表时间: 2021
• 期刊: arXiv:2104.08809
• 作者: Cattan, A.;Johnson, S.;Weld, D.S.;Dagan, I.;Beltagy, I.;Downey, D.;Hope, T.
• 通讯作者: Hope, T.

From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks

• DOI: 10.1145/3491102.3517470
• 发表时间: 2022-04
• 期刊: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
• 作者: Hyeonsu B Kang;Rafal Kocielnik;Andrew Head;Jiangjiang Yang;Matt Latzke;A. Kittur;Daniel S. Weld;Doug Downey;Jonathan Bragg