# A Chinese Named Entity Recognition Method Based on ERNIE-BiLSTM-CRF for Food Safety Domain
## 5 Conclusions  
In order to obtain as many practical features as possible from the noisy mixed small-scale corpus to improve the performance of named entity recognition of unstandardized Chinese text, we propose the ERNIE-Adv-BiLSTM-Att-CRF model:  
* ERNIE, a pre-trained model with advantages for modelling unstandardized Chinese text, is chosen to generate context-based word vectors that retain rich implicit information;
* Adversarial training is added to the model training as a regularization tool to alleviate the effect of dataset noise on the NER model;
* Self-attention is added to the BiLSTM network to automatically focus on the features that have a decisive impact on entity classification and encode them in sequence;
* Sequence decoding is performed in the CRF layer to obtain the best label corresponding to each token.  
The experimental results show that our approach obtains SOTA performance on the public dataset Weibo NER with an F1 value of 72.64%. As shown in Table 3, our model outperforms other SOTA models on the microblog NER dataset. Compared with the FLAT, SLK-NER, Locate and Label, and AESINER models, the F1 values obtained significant improvements of 12.32%, 8.64%, 3.48%, and 2.86%, respectively; compared with the BERT-LMCRF, FLAT + BERT, SA-NER, and FGN models, our F1 values also obtained significant improvements of improved by 5. 52%, 4.09%, 2.84%, and 1.39%, respectively. Good performance was also obtained on the self-built in-domain dataset Food, with an F1 value of 69.68%. As can be seen from Table 4, our model is significantly higher than the other two baseline models in terms of F1 values, with improvements of 3.61% and 1.96%, respectively. The effectiveness of our method in the non-standardized NER task is fully demonstrated. Moreover, the performance of different pre-trained models and adversarial training methods are discussed in the ablation experiments.  
Based on the experiments in this paper, it is demonstrated that our model can extract specific entity information from non-standardized web texts. This is useful for NER tasks in corpus-poor domains, such as the food safety domain. To address the problem that there is no publicly available NER dataset in the food safety domain, our approach can extract named entities related to food safety from short non-standardized Chinese texts generated from web users' comments. In this way, a regulatory knowledge graph in the food safety domain can be constructed to help relevant authorities to regulate food safety issues and mitigate the harm caused by food safety problems.  
**Author Contributions:** Conceptualization and methodology, T.Y.; writing\(-\)original draft preparation, T.Y.; investigation, T.Y., C.W. and X.Q.; resources and supervision X.Q.; data curation, T.Y. and C.W.; project administration, X.Q.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is supported by the Major science and technology special projects of Xinjiang Uygur Autonomous Region (2020A03001) and its sub-program Key technology development and application demonstration of integrated food data supervision platform in Xinjiang region (2020A03001-2).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to express our special thanks to the director of the Xinjiang Uygur Autonomous Region Institute of Product Quality Supervision and Inspection, [PERSON], for his advice on the self-built food safety NER dataset in this paper and his participation in the project management of this paper's support project, the Xinjiang Region Food Big Data Comprehensive Supervision Platform Key Technology Development and Application Demonstration (2020A03001-2).  
**Conflicts of Interest:** The authors declare no conflict of interest.