Event-driven document selection for terrorism

• Main Authors: SUN, Zhen, LIM, Ee Peng, CHANG, Kuiyu, ONG, Teng-Kwee, Gunaratna, Rohan Kumar
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2005
• Subjects: Artificial intelligence; Pattern extraction; Entity relationship model; Document selection; Information extraction; Terrorism; Reactive system; Computer security; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/890; https://ink.library.smu.edu.sg/context/sis_research/article/1889/viewcontent/Sun2005_Chapter_Event_DrivenDocumentSelectionF.pdf
• Institution: Singapore Management University

In this paper, we examine the task of extracting information about terrorism related events hidden in a large document collection. The task assumes that a terrorism related event can be described by a set of entity and relation instances. To reduce the amount of time and efforts in extracting these event related instances, one should ideally perform the task on the relevant documents only. We have therefore proposed some document selection strategies based on information extraction (IE) patterns. Each strategy attempts to select one document at a time such that the gain of event related instance information is maximized. Our IE-based document selection strategies assume that some IE patterns are given to extract event instances. We conducted some experiments for one terrorism related event. Experiments have shown that our proposed IE based document selection strategies work well in the extraction task for news collections of various size.