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，这使得研究人员难以跟上。人们对能够从非结构化文本中自动提取结构化信息的信息提取系统有着很高的需求。自动信息提取 (IE) 的好处是多方面的：更容易搜索和组织科学文档，提高策展人的效率，并降低策展成本等。尽管基于监督深度学习的 IE 方法在某些应用程序上实现了管理级性能，但需要具有准确注释的大型训练数据集才能实现这些结果。该项目的目标是开发一个适应性强且灵活的信息提取框架，该框架可以从现有资源中学习，而不依赖于昂贵且耗时的专家注释，并弥合实际应用程序中的性能差距，解决提取质量问题和 IE 的独特要求科学文献中的任务。该项目的成功将通过提供处理大量未标记文本数据集的机制、加快文献理解和管理过程以及促进新的科学发现来使许多领域受益。研究员将参与部门扩大计算参与 (BPC) 活动，并根据该项目的结果创建教育材料，用于当地 k-12 学校和社区的推广计划。该项目侧重于三个互补的研究重点，每个重点都涉及科学文献信息提取的一个主要障碍：1）在利用所有现有资源的同时，推进 IE 模型以处理异构监督，例如远程监督和间接监督，2）开发新的半开放信息提取任务以提取详细的上下文和信息的不确定性文档级别；3）开发一种新颖的从错误中学习的范例，该范例集成一阶逻辑规则和来自领域用户的新注释，以细化 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，这将为机器学习、数据挖掘和自然语言处理带来新的原理、方法和技术。研究重点将应用于从 STEM 教科书中提取信息，构建用于教育目的的概念网络。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。