Precedes: A Semantic Relation in FrameNet

Automatic language processing systems depend on, among others factors, the effectiveness in modeling human cognitive abilities, including the capacity to draw inferences about prototypical or expected sequences of events and their temporal order. Appropriate response to a crisis is as important for public security as are efforts to prevent any such natural or man made disaster. Recent research (Mehrota et al. 2008) has recognized the need for accurate and actionable situation awareness during emergencies, where timely status updates are critical for effective crisis management. The present paper constitutes a contribution to situation awareness for Natural Language Processing (NLP) applications to improve communication among first responders, and features the frame-to-frame semantic relation Precedes, as implemented in FrameNet (http://framenet.icsi.berkeley.edu). Specifically, this work demonstrates the necessity and importance of the information encoded with Precedes for NLP applications, advocating the inclusion of such information in systems for security applications.