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.