Automatic Emergency Information Classification Based on Rules and Maximum Entropy Model
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.