RI: Small: Describing Disasters and the Ensuing Personal Toll

该项目的研究目标是通过开发一种能够自动生成灾害描述的系统来帮助人们对重大自然灾害和人为灾害做出反应，无论是人为灾害还是气候事件造成的灾害。通过在灾害发生几个月后生成灾害影响描述，该系统可以记录不同国家和城市不同地点不同规模灾害的影响。描述的信息来自新闻和社交媒体。描述是从客观的角度讲述的，描述已知的事实，从个人的角度描述经历过灾难的人的经历和情感。通过获取个人故事来丰富对灾难影响的描述，提供了关于灾难如何影响个人的令人信服的看法。通过生成灾难发生时的影响描述，救援组织可以更好地协调将援助运送到需要援助的地方。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。该项目正在创造具有社会公益潜力的技术，因此将吸引许多学生，包括寻求对社会做出有意义贡献的本科生。该项目将研究如何生成突出大型活动结构的描述与自动识别引人入胜的个人故事的研究结合在一起。使用了半监督和监督的方法来解决这个问题，利用了大规模的在线数据来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，这些方法利用哥伦比亚大学的Newsblaster系统11年来生成的摘要以及其他在线大规模语义信息。它的特点是构建一棵事件树，给出来自新闻和社交媒体的文本描述，其中节点表示事件(包括较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，它使用一种有监督的方法来学习，当一篇取自社交媒体的文本传达了一个基于叙事结构的社会语言学理论的有趣故事时。

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.