SBIR Phase I: scite: A deep learning platform to identify confirming and refuting citations

SBIR第一期项目旨在创建一种资源，使非专家也能评估科学主张的有效性。目前衡量科学可靠性的方法——一篇科学报告被引用的次数和发表该报告的期刊的声望——被认为是本质上较差的研究质量指标，但是由于缺乏更好的替代方法，仍然在使用。这一缺陷使公众、企业和政府损失了数十亿美元，并错失了机会，因为利用无效声明来发现新的药物和技术不太可能成功。拟议的研究和由此产生的原型将通过自动从科学文献中提取提供支持或反驳证据的分类陈述，帮助非专家评估科学主张。如果成功，拟议中的创新将大大增加能够对其进行合理评估的人数，从而鼓励可靠的研究，将使个人和组织能够根据科学证据做出更明智的决定，并将有助于教育专业人员和公众了解他们专业领域以外的现有证据。这个SBIR一期项目将使用深度学习和自然语言处理的最新发展来创建一个平台，可以以以前无法实现的速度和规模自动分析科学主张的准确性。这一创新将使该项目能够分析报告的科学主张，在公共资源中收集结果信息，并通过基于web的直观用户界面以易于掌握的形式向最终用户提供这些信息。所提议的研究的具体目标是使分类语句的准确性，支持，反驳，或仅仅提到一个人类的科学主张方法，这将要求该项目进一步推进深度学习和文本分析，以提高从科学文献中提取这些语句的效率，并开发以直观的图形形式可视化准确性分析结果的方法。尽管文本挖掘技术迅速发展，但由于科学报告的特定语言和报告分发的特殊性，分析科学报告的文本在技术和逻辑上仍然具有挑战性。成功完成该项目不仅将产生一个急需的工具，而且将推动科学引文情感分析的新兴领域。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。

This SBIR Phase I project is directed towards creating a resource that would enable non-experts to evaluate the validity of scientific claims. The current measures of scientific reliability - the number of times a scientific report is cited and the prestige of the journal that publishes it - are known to be inherently poor indicators of research quality but are still used for the lack of a better alternative. This deficiency costs the public, businesses, and governments billions of dollars and missed opportunities because using invalid claims to discover new medicines and technologies is unlikely to be successful. The proposed research and the resulting prototype will help a non-expert to evaluate scientific claims by automatically extracting classifying statements from scientific literature that provide supporting or contradicting evidence. If successful, the proposed innovation will encourage reliable research by vastly increasing the number of people who can evaluate it soundly, will enable individuals and organizations make better-informed decisions based on scientific evidence, and will help to educate both professionals and the public about available evidence outside their areas of expertise. This SBIR Phase I project will use the latest developments in deep learning and natural language processing to create a platform that can automatically analyze the veracity of scientific claims at previously unachievable speed and scale. This innovation will allow this project to analyze reported scientific claims, to collect the resulting information in a publicly available resource, and to provide this information to end users through a web-based intuitive user interface in an easy to grasp form. The specific aims of the proposed research are to make the accuracy of classifying statements that support, contradict, or merely mention a scientific claim approach that of a human, which will require this project to advance deep learning and text analysis even further, to increase the efficiency of extracting these statements from scientific literature, and to develop approaches for visualizing the results of veracity analysis in an intuitively graphical form. Despite rapid advances in text mining technology, analyzing the text of scientific reports is still challenging, both technically and logistically, due to their specific language and the peculiarities of report distribution. Accomplishing this project successfully will not only produce a highly needed tool but will also advance the emerging field of sentiment analysis of scientific citations.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.