CRII: RI: Subevent Acquisition and Analysis

CRII：RI：子事件采集和分析

• 批准号: 1755943
• 负责人: Ruihong Huang
• 金额: $ 17.31万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755943&HistoricalAwards=false
• 关键词: CRII; RI; Subevent; Acquisition; Analysis

Subevents that describe physical actions composing an event, widely exist in event descriptions and provide the evidence that an event is occurring. For example, if a person were to explain how he knew that the crowd gathered in the street was rioting, he might point to the shouting of political slogans or tires lit on fire. In this instance, the riot would be the event. The slogan-shouting and tire-burning would be the subevents. Knowledge of subevents provides important information for characterizing events, and has great potential to benefit various event oriented applications, such as event detection, event visualization, event summarization and extreme event management. This project aims to acquire subevents from text corpora by recognizing various contextual patterns that contain subevents. This research on subevent identification will bring us one step closer to automatic event ontology construction, which organizes events based on their relations (subevent, precursor, consequence, cause, condition and purpose, etc.) with a central type of event. This project will integrate research with education, train and prepare the future NLP researchers as well as expose early undergraduate students and high school students to NLP research, with a focus on broadening participation of underrepresented groups.An initial subevent extractor has been developed that leverages a specific type of sentential and local context to recognize subevents. This project will extend the approach to explore diverse types of contexts that suggest subevents, by incorporating event coreference resolution into the learning loop. The task of event coreference resolution aims to identify event mentions that refer to the same real world event, and coreferential event mentions can appear in distinct contexts from the same document or from multiple documents. In the proposed research, new contexts identified through event coreference resolution that contain subevents will be used to acquire more subevents from a text corpus. In addition, this project will conduct extensive analysis of learned subevents. The project will apply clustering algorithms as well as analyze document samples to understand the usefulness of subevents in categorizing and identifying properties of the parent event as well as in identifying causal relations involving the parent event.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.

描述构成事件的物理动作的子事件广泛存在于事件描述中，并提供事件正在发生的证据。例如，如果一个人要解释他如何知道聚集在街上的人群正在暴乱，他可能会指出政治口号的呼喊或点燃的轮胎。在这种情况下，骚乱将成为事件。喊口号和烧轮胎是次要的活动。子事件的知识为描述事件提供了重要的信息，并且对各种面向事件的应用程序（如事件检测、事件可视化、事件总结和极端事件管理）有很大的好处。本项目旨在通过识别包含子事件的各种上下文模式，从文本语料库中获取子事件。子事件识别的研究将使我们离事件本体的自动构建更近一步，即根据事件与中心类型的关系（子事件、前体、结果、原因、条件、目的等）来组织事件。该项目将把研究与教育结合起来，培训和准备未来的NLP研究人员，并让早期的本科生和高中生接触到NLP研究，重点是扩大代表性不足群体的参与。已经开发了一个初始子事件提取器，它利用特定类型的句子和本地上下文来识别子事件。该项目将扩展该方法，通过将事件共同参考解析纳入学习循环，探索暗示子事件的不同类型的上下文。事件共引用解析的任务旨在识别引用相同现实世界事件的事件提及，而共引用事件提及可以出现在来自同一文档或多个文档的不同上下文中。在本研究中，通过包含子事件的事件共引用解析识别的新上下文将用于从文本语料库中获取更多的子事件。此外，本项目将对所学的子事件进行广泛的分析。该项目将应用聚类算法以及分析文档样本，以了解子事件在分类和识别父事件属性以及识别涉及父事件的因果关系方面的有用性。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。

项目成果

Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management

• DOI: 10.1609/aaai.v34i01.5391
• 发表时间: 2020-04
• 期刊: ArXiv
• 作者: Wenlin Yao;Cheng Zhang;S. Saravanan;Ruihong Huang;A. Mostafavi

Automatic Data Acquisition for Event Coreference Resolution

• DOI: 10.18653/v1/2021.eacl-main.101
• 发表时间: 2021
• 作者: Prafulla Kumar Choubey;Ruihong Huang

A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing

• DOI: 10.18653/v1/d19-1295
• 发表时间: 2019-11
• 期刊: IEEE Transactions on Image Processing
• 影响因子: 10.6
• 作者: Zeyu Dai;Ruihong Huang

Building Context-aware Clause Representations for Situation Entity Type Classification

• DOI: 10.18653/v1/d18-1368
• 发表时间: 2018-09
• 期刊: ArXiv
• 作者: Zeyu Dai;Ruihong Huang

Weakly Supervised Subevent Knowledge Acquisition

• DOI: 10.18653/v1/2020.emnlp-main.430
• 发表时间: 2020-11
• 作者: Wenlin Yao;Zeyu Dai;Maitreyi Ramaswamy;Bonan Min;Ruihong Huang