CAREER: Discourse Level Event-Event Relation Identification

从自然语言文本中理解事件（抗议、选举、疾病爆发、自然灾害）是重要分析任务的关键，例如预测未来事件、检测假新闻和其他验证事件的尝试、管理极端事件、回答复杂问题以及生成用于分析的简明文本摘要。现有的事件提取系统侧重于识别孤立事件，但很少考虑事件之间的关系。因此，提取出来的事件仅仅是描述谁做了什么事情的事实，但很难解释这些事件是如何发生的以及为什么发生的。事实上，对事件的描述往往与其他事件有着复杂的关系，例如，如果新闻报道了暗杀事件而没有提及该事件是如何进行的，或者如果它们描述了抗议事件而没有说明其发起原因，则新闻文章是不完整的。这个教师早期职业发展项目旨在生成文档级事件图，捕获文档中任何地方提到的事件之间的丰富关系，这将使我们能够将事件上下文化，将事件提取从简单地提取单个事件事实转换为提取信息丰富的上下文事件解释，并更好地支持各种面向事件的应用程序。该项目将把研究与教育结合起来，用先进的信息提取观点和方法培训和培养未来的研究人员，并让大量不同的本科生和高中生接触计算机科学和自然语言处理研究，重点是显著扩大少数民族和代表性不足群体的参与。构建文档级事件图需要识别两个事件之间的关系，即使它们相隔几个句子，这就提出了多个技术挑战。本项目将为话语感知事件-事件关系识别奠定基础，研究事件-事件关系与话语结构不同维度之间的相关性。本研究的动机是观察到事件是形成连贯故事的主要材料，事件的存在与文献的整体话语结构密切相关。该项目开发了监督和非监督学习方法来构建有效的话语级事件-事件关系识别器。具体来说，该项目开发了话语引导方法来识别两种重要类型的事件-事件关系，共参考和时间顺序，这是构建有意义的事件图的基础。然后，在通过监督学习获得的事件话语相关性的指导下，开发了无监督学习方法，该方法可以有效地利用大量未标记数据，处理词汇多样性问题并提高系统对事件-事件关系识别的鲁棒性。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。

Understanding events (protests, elections, disease outbreaks, natural disasters) from natural language text is key to important analytic tasks like predicting future events, detecting fake news and other attempts to validate events, managing extreme events, answering complex questions and generating concise text summaries for analysis. Existing event extraction systems focus on identifying isolated events, but have rarely considered relations between events. Consequently, the extracted events are merely facts describing who did what, but it is hard to interpret how and why those events happened. Indeed, events tend to be described in a complex relationship with other events, for example, news articles are incomplete if they report an assassination event without mentioning how the event was conducted, or if they describe a protest event without information on why it was launched. This Faculty Early Career Development project aims to generate document-level event graphs that capture rich relations between events mentioned anywhere in a document, which will enable us to contextualize events, transform event extraction from simply extracting individual event facts to extracting informative context-rich event interpretations, and better support various event-oriented applications. The project will integrate research with education, train and prepare future researchers with advanced information extraction views and methods, as well as expose a large number of diverse undergraduate students and high school students to computer science and natural language processing research with a focus on significantly broadening participation of minorities and underrepresented groups. Building document-level event graphs requires identifying relations between two events even when they are sentences away, which presents multiple technical challenges. This project will lay the foundation for discourse-aware event-event relation identification, and study correlations between event-event relations and different dimensions of discourse structures. The research is motivated by the observation that events are major materials in forming a cohesive story and the presence of events is tightly correlated with the overall discourse structure of a document. The project develops both supervised and unsupervised learning methods to build effective discourse level event-event relation recognizers. Specifically, the project develops discourse guided approaches to identify two important types of event-event relations, coreference and temporal ordering, which are fundamental for building meaningful event graphs. Then, guided by event discourse correlations obtained via supervised learning, unsupervised learning methods are developed that can effectively make use of large volumes of unlabeled data, deal with lexical diversity issues and improve robustness of systems for event-event relation identification.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.