CAREER: Learning to Extract Consistent Event Graphs from Long and Complex Documents

职业：学习从长而复杂的文档中提取一致的事件图

• 批准号: 2340435
• 负责人: Xinya Du
• 金额: $ 56.12万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2029-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340435&HistoricalAwards=false
• 关键词: CAREER; Learning; Extract; Consistent; Event

Documents about real-world events are published daily. The large number of such documents makes it very hard for people to read and absorb them all, a phenomenon known as “information overload". Applying computer algorithms that can automatically extract events is a promising solution because they can transform large amounts of text into smaller summaries in the form of structured event knowledge graphs that reveal the relationships between the people, places, and times in the events. Current deep learning-based event extraction techniques mainly focus on extracting event knowledge at the level of individual sentences and are unable to extract a knowledge graph spanning multiple sentences with sufficient accuracy or efficiency. For example, existing techniques would struggle with events described in a long document having multiple sections. Moreover, these extraction techniques do not capture accurate information regarding real-life events because they typically include nuanced attributes such as causes and effects. The research goal of this CAREER award is to build information extraction (IE) methods with natural language processing methods, using the latest deep learning-based techniques, to construct an event knowledge graph for storing knowledge and improving the ability of people to track rapidly evolving event information. In the short term, the project will improve the quality and comprehensiveness of event knowledge graphs. In the long run, the project will entirely transform people's experiences and habits in acquiring event knowledge from various sources. The system to be developed through this award will better support numerous event-oriented tasks that people need to perform, such as future event prediction, event factuality verification, and risk event prevention, all of which have profound impacts on society. Moreover, our work would make fundamental contributions to a wide range of interdisciplinary applications such as statutory reasoning based on legal documents, prediction of disease outbreaks, and biomedical document understanding, all of which currently rely on extremely slow and high-cost methods.The general technical goal of this project is to address the knowledge gap of event extraction from long and complex documents (as compared to the traditional sentence-level extraction) and to do so in an efficient manner. The general goal is divided into three sub-research goals. First, to extract the entirety of event attributes, which is not possible for current models trained on a dataset with a predefined schema, the project introduces a new question-answer generation paradigm that enables a novel representation of events from clusters of documents discussing the same events. The project will leverage document hierarchy information for extracting events, which enforces the validity and broad coverage of event information. Motivated by the fact that current event knowledge construction is inefficient and is impaired by pairwise event-event relation predictions, the second research goal is to develop novel techniques enabling the construction of the event knowledge graph. For this purpose, the investigators propose interleaving targeted retrieval and joint modeling of event arguments and entity-entity relations. This not only enables efficient updating of graphs, but also ensures its global consistency. Finally, the third goal is to adapt to individual information-seeking needs, which is not considered by current methods. The project will study schema induction strategies and schema matching algorithms for adapting the event knowledge graph to user preferences.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.

关于现实事件的文档每天都被称为“信息过载”。 ，在事件中的位置和时间。以及使用最新的基于深度学习的技术来构建一个用于存储和改善tople tople tople tople tople tople的事件事件信息的典型的细微属性，例如原因和效果。从长远来看，从各种来源获得事件，将改善知识图的综合性。事件的事实验证和风险预防，所有这些都会对社会产生深远的影响。为了添加从长长而复杂的文档到传统句子级提取的知识差距（成三个子研究目标。为了提取分配的启动式ha ha ha ha ha ha predphendess架构，该项目介绍了一个新的问答生成范式，可以启用一个启用一个讨论相同事件的事件的新颖性。为了开发新的事件知识图的构造，研究人员交织了目标和实体关系的有效更新的目标检索和建模。使用研究模式指示策略和模式匹配算法，以将事件知识图适应为用户偏好。该奖项反映了NSF'Stututory和值得支持的主题，值得支持的是，值得支持的是，值得支持的宗旨和更广泛的影响。