Abstract
Text classification is the basic work of researching on the evolution of emergencies. While, in emergency information management systems, most of the emergency information is currently expressed in a text form and always manually classified, which might result in poor efficiency and low accuracy. To deal with it, this paper proposes an automatic emergency text classification method based on rules and Maximum Entropy Model (MEM). First, according to the Public Safety Triangular Framework (PSTF) theory, an emergency information constitution mechanism is established. Then, based on it, the corresponding feature words base is built and is used for constructing the Event Rules Base (ERB). ERB acts as classification basis and is input into MEM for training to generate the emergency text classifier. Experiment results on real data demonstrate that the proposed classification method is able to yield good performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: DEStech Transactions on Engineering and Technology Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
