Affiliations: [a]
School of Software, University of Xinjiang, Xinjiang, China
| [b]
College of Network Center, University of Xinjiang, Xinjiang, China
| [c]
Institute of Big Data Research, University of Central South, Changsha, China
| [c]
Internet Information Security Centre, Xinjiang, China
Correspondence:
[*]
Corresponding author. Shengwei Tian, School of Software, University of Xinjiang, Xinjiang, China. E-mail: [email protected]
Abstract: With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model.
Keywords: Named Entity Recognition, BERT, BILSTM, CRF, Adversarial Training
