Abstract
In this paper we introduce an information analysis system that automatically acquires from social media like Twitter the kind of vital information that rescue workers or disaster victims need in case of large-scale disasters like earthquakes or tsunami. This system uses question-answering (QA) technology with the aim of helping users get a comprehensive overview of the state of affairs and the various conditions of ongoing rescue efforts in the afflicted areas, and detect potentially unanticipated information from the disaster areas. The system expands the input question with various entailment expressions, and augments the original social media input data by analyzing the mentioned place names in order to handle a wide variety of questions relevant to disaster scenarios. Moreover, we extend this system to address rescue workers’ crucial problem of getting access to relevant information from the disaster areas in times of crisis. To tackle this problem we introduce a mechanism by which rescue workers from NPOs or municipalities can register certain questions for situation assessment in advance, so that when a disaster victim posts some urgent requests for food, medicines or other essentials on Twitter or some other BBS, both information sender and information requester are automatically notified of this. We expect that such a mechanism can safeguard the two-way communication between rescue workers and disaster victims, and ultimately lead to a more effective rescue effort. We evaluate the system on a test set of 300 questions and their answers. For 192 questions whose answers are actually included in our system’s index, we obtained on average 605.8 answers per question, with 51.9% recall and 60.8% precision.
