Chinese named entity recognition method for the field of network security based on RoBERTa

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively.