CAREER: Achieving Quality Information Extraction from Scientific Documents with Heterogeneous Weak Supervisions

职业：通过异构弱监督实现科学文档中的质量信息提取

• 批准号: 2237831
• 负责人: Qi Li
• 金额: $ 49.99万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237831&HistoricalAwards=false
• 关键词: CAREER; Achieving; Quality; Information; Extraction

The volume and breadth of the scientific literature is growing at an astonishing pace, making it challenging for researchers to keep up. Information extraction systems that can automatically extract structured information from this unstructured text are in high demand. Benefits from automated information extraction (IE) are multi-fold: it is easier to search and organize scientific documents, it results in efficiency gains for curators, and it reduces curation costs, among others. Although supervised deep learning-based IE methods achieve curation-level performance on some applications, large training datasets with accurate annotations are necessary to achieve these results. The goal of this project is to develop an adaptable and flexible information extraction framework that learns from existing resources and does not rely on costly and time-consuming expert annotations, and bridges the performance gap in real applications addressing extraction quality concerns and unique requirements of IE tasks in the scientific literature. Success in this project will benefit many domains by providing mechanisms for processing massive unlabeled textual datasets, speeding up literature understanding and the curation process, and promoting new scientific discoveries. The investigator will engage in departmental Broadening Participation in Computing (BPC) activities and create educational materials based on results from this project for outreach programs to local k-12 schools and communities.This project is focused on three complementary research thrusts, each of which addresses one key obstacle of information extraction on scientific documents: 1) advancing IE models to work with heterogeneous supervisions such as distant supervision and indirect supervision while taking advantage of all existing resources, 2) developing new semi-open information extraction tasks to extract detailed context and uncertainties at the document level, and 3) developing a novel learn-from-mistake paradigm that integrates first-order logic rules and new annotations from domain users to refine the IE models and results. The proposed research will address a variety of problems drawn from different information extraction settings, which will lead to new principles, methods, and technologies for machine learning, data mining, and natural language processing. The research thrusts will be applied to extract information from STEM textbooks to construct concept networks for education purposes.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.

科学文献的数量和广度正在以惊人的速度增长，这使研究人员保持挑战。可以自动从此非结构化文本中提取结构化信息的信息提取系统需求很高。自动化信息提取（IE）的好处是多重的：搜索和组织科学文档更容易，从而为策展人带来效率的提高，并且降低了策展成本等。尽管有监督的基于深度学习的IE方法在某些应用程序上实现了策划级别的性能，但必须进行准确注释的大型培训数据集以实现这些结果。该项目的目的是开发一个适应性且灵活的信息提取框架，该框架从现有资源中学习，不依赖于昂贵且耗时的专家注释，并在科学文献中解决了提取质量问题和IE任务的独特要求的实际应用程序中的性能差距。该项目的成功将通过提供处理大量未标记的文本数据集，加快文学理解和策展过程并促进新的科学发现的机制来使许多领域受益。研究人员将根据该项目的结果来开展部门扩大计算活动（BPC）活动的参与并创建教育材料，向本地K-112学校和社区进行外展计划。该项目侧重于三个互补的研究推力，每项都针对各个信息的障碍，涉及有关科学范围的一项关键障碍，以促进科学范围内的分类范围，以促进与现有的IE E EE型模型，并将其分类范围内的IE EE索引IE构图，并将其范围用于既定的IE EE范围。 2）开发新的半开放信息提取任务，以在文档级别提取详细的上下文和不确定性，3）开发一种新颖的学习范围，从范围内整合了一阶逻辑规则和域用户的新注释，以完善IE模型和结果。拟议的研究将解决来自不同信息提取设置的各种问题，这将导致用于机器学习，数据挖掘和自然语言处理的新原理，方法和技术。该研究的推力将用于从STEM教科书中提取信息，以构建用于教育目的的概念网络。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。