Research Article
An OOV Recognition Based Approach to Detecting Sensitive Information in Dialogue Texts of Electric Power Customer Services
@INPROCEEDINGS{10.1007/978-3-030-69066-3_52, author={Xiao Liang and Ningyu An and Ning Wu and Yunfeng Zou and Lijiao Zhao}, title={An OOV Recognition Based Approach to Detecting Sensitive Information in Dialogue Texts of Electric Power Customer Services}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Out-of-vocabulary Sensitive word recognition HowNet Word embedding Electric power customer services}, doi={10.1007/978-3-030-69066-3_52} }- Xiao Liang
Ningyu An
Ning Wu
Yunfeng Zou
Lijiao Zhao
Year: 2021
An OOV Recognition Based Approach to Detecting Sensitive Information in Dialogue Texts of Electric Power Customer Services
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_52
Abstract
Sensitive word recognition technology is of great significance to the protection of enterprise privacy data. In electric power custom services systems, the dialogue texts recording the conversational information between electric power customers and the customer services staffs contain some sensitive information of electric power customers. However, the colloquialism and synonyms in dialogue texts often make sensitive information recognition more difficult. In this paper, we proposed an out-of-vocabulary (OOV) approach for recognizing sensitive words in the dialogue texts of electric power customer services. We combine the semantic similarity based on word embeddings and structural semantic similarity based on HowNet for recognizing sensitive OOV words in the dialogue texts. The related experiments were made, and the experimental results show that our method has higher recognition accuracy in comparison with the popular approaches.