G2MBCF: Enhanced Named Entity Recognition for sensitive entities identification

With the increasing growth of data, work on data security is becoming increasingly important. As the core of important data detection, the sensitive entities identification (SEI) problem has become a hot topic in natural language processing (NLP) science. Named Entity Recognition (NER) is the foundation of SEI, however, current studies treat SEI only as a special case of the NER problem. It lacks more detailed considerations of implicit links between entities and relations. In this paper, we propose a novel enhanced method called G2MBCF based on latent factor model (LFM). We use knowledge graph to represent the NER primary result with semantic structure. Then we use G2MBCF to inscribe entities and relations through a $E-R$ matrix to mine implicit connections. Experiments show that compared to existing NER methods, our method enhances $Recall$ and $Precision$ of SEI. We also studied the influence of parameters in the experiments.