RI: Small: Describing Disasters and the Ensuing Personal Toll

RI：小：描述灾难和随之而来的个人损失

• 批准号: 1422863
• 负责人: Kathleen McKeown
• 金额: $ 42.3万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422863&HistoricalAwards=false
• 关键词: RI; Small; Describing; Disasters; Ensuing

The research goal of this project is to help people react to major natural and man-made disasters by developing a system that can automatically generate descriptions of disasters, whether man-made or resulting from climate events. Through the generation of descriptions of disaster impact several months after the disaster has occurred, the system can document the impact of different scale disasters in different locations from country to city. Information for the description is drawn from news and social media. Descriptions are told from an objective point of view, describing the facts as known, and from a personal point of view, describing the experiences and emotions of people who experienced the disaster. Enriching the descriptions of disaster impact by access to personal stories provides a compelling look at how disasters impact individuals. Through generation of descriptions of the impact of a disaster as it happens, relief organizations can better coordinate delivery of aid to where it is needed. The generated descriptions are being made available through a public website so that all people interested in impact of a disaster will have access. This project is creating technology with the potential for social good and thus, will have appeal to many students, including undergraduates who seek to make their contributions meaningful to society.The projet brings together research on generating descriptions that highlight the structure of large-scale events with research on automatic identification of riveting personal stories. Semi-supervised and supervised approaches to the problem are used, drawing on large-scale online sources of data as well as smaller collections of annotated data. The project features the use of semi-supervised approaches to learning event relevance that exploit 11 years of summaries generated by Columbia's Newsblaster system plus other online large-scale semantic information. It features construction of an event tree given textual descriptions from news and social media where nodes represent events (both larger scale and sub-events) and the tree structure can represent subsumption, temporal and causal relations between events. Finally, it uses a supervised approach to learning when a text taken from social media conveys an interesting story based on a socio-linguistic theory of narrative structure.

该项目的研究目标是通过开发一个系统来帮助人们应对重大的自然灾害和人为灾害，该系统可以自动生成灾害的描述，无论是人为的还是由气候事件引起的。通过在灾害发生几个月后生成灾害影响描述，系统可以记录从农村到城市不同地点不同规模的灾害的影响。该描述的信息来自新闻和社交媒体。叙述从客观的角度出发，描述已知的事实，从个人的角度出发，描述经历灾难的人的经历和情感。通过获取个人故事来丰富对灾害影响的描述，可以令人信服地了解灾害如何影响个人。通过对灾难发生时影响的描述，救援组织可以更好地协调向需要的地方提供援助。生成的描述将通过一个公共网站提供，以便所有对灾难影响感兴趣的人都可以访问。这个项目正在创造具有社会效益潜力的技术，因此，将吸引许多学生，包括那些寻求对社会做出有意义贡献的本科生。该项目结合了对大型事件结构的生成描述和对引人入胜的个人故事的自动识别的研究。使用半监督和监督方法来解决问题，利用大规模的在线数据源以及较小的注释数据集合。该项目的特点是使用半监督方法来学习事件相关性，该方法利用了哥伦比亚大学Newsblaster系统11年来生成的摘要以及其他在线大规模语义信息。它的特点是根据新闻和社交媒体的文本描述构建事件树，其中节点表示事件（包括更大规模的事件和子事件），树结构可以表示事件之间的包容、时间和因果关系。最后，当从社交媒体上获取的文本传达一个基于叙事结构的社会语言学理论的有趣故事时，它使用一种监督的方法来学习。