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

科学文献的数量和广度正以惊人的速度增长，这对研究人员来说是一个挑战。能够从这些非结构化文本中自动提取结构化信息的信息提取系统的需求很大。自动化信息提取（IE）的好处是多方面的：更容易搜索和组织科学文档，提高了管理人员的效率，降低了管理成本等。尽管基于监督的深度学习的IE方法在某些应用程序上实现了策展级的性能，但要实现这些结果，需要具有准确注释的大型训练数据集。该项目的目标是开发一种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，并弥合实际应用中的性能差距，解决科学文献中IE任务的提取质量问题和独特需求。这个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，加速文献理解和管理过程，促进新的科学发现，从而使许多领域受益。研究人员将参与部门的“扩大计算机参与”（BPC）活动，并根据该项目的结果制作教育材料，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，每一个重点都解决了科学文献信息提取的一个关键障碍：1)在利用所有现有资源的同时，推进IE模型与异构监督（如远程监督和间接监督）一起工作；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)开发一种新的从错误中学习的范式，该范式集成了一阶逻辑规则和领域用户的新注释，以改进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，这将导致机器学习，数据挖掘和自然语言处理的新原理，方法和技术。研究重点将应用于从STEM教科书中提取信息，以构建用于教育目的的概念网络。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。

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.