Named Entity Recognition for Vulnerabilities Based on BLSTM-CRF Model

Abstract:

Unstructured text resources provide a large amount of information related to vulnerability. Traditional domain-specific entity recognition relies on feature templates and domain knowledge to recognize related entities. The recognition performance depends largely on the quality of manually selected feature functions. It is a challenging task to mine the features implied by the text automatically, rather than manually formulate the characterization of the domain terminology. In this paper, a BLSTM and CRF security vulnerability domain entity recognition model (BLSTM-CRF model) is proposed and a dictionary is used to correct the results generated by the model. The F value can reach 85%. Experiments show that this method can significantly reduce the workload of manually selecting features while improving the precision and recall