KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT

Author:

Cong Kai1ORCID,Li Tao2ORCID,Li Beibei2ORCID,Gao Zhan1ORCID,Xu Yanbin1ORCID,Gao Fei2ORCID

Affiliation:

1. College of Computer Science, Sichuan University, Chengdu 610065, China

2. School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China

Abstract

The Bidirectional Encoder Representations from Transformers (BERT) technique has been widely used in detecting Chinese sensitive information. However, existing BERT-based frameworks usually fail to emphasize key entities in the texts that contribute significantly to knowledge inference. To meet this gap, we propose a BERT and knowledge graph-based novel framework to detect Chinese sensitive information (named KGDetector). Specifically, we first train a pretrained knowledge graph-based Chinese entity embedding model to characterize entities in the Chinese textual inputs. Finally, we propose an effective framework KGDetector to detect Chinese sensitive information, which employs the knowledge graph-based embedding model and the CNN classification model. Extensive experiments on our crafted Chinese sensitive information dataset demonstrate that KGDetector can effectively detect Chinese sensitive information, outperforming existing baseline frameworks.

Funder

National Basic Research Program of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

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