%A Hu Haotian,Ji Jinfeng,Wang Dongbo,Deng Sanhong %T An Integrated Platform for Food Safety Incident Entities Based on Deep Learning %0 Journal Article %D 2021 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2019.1031 %P 12-24 %V 5 %N 3 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_5040.shtml} %8 2021-03-25 %X

[Objective] This paper tries to promote the national administration of food safety, and strengthen the prediction, warning and response of related emergencies. It not only facilitates research, but also informs the public on food safety issues concisely and intuitively. [Methods] We collected news reports on food safety incidents from leading websites and constructed a corpus for the food safety incident entities through data cleansing, annotation, and organization. Then, we compared performance of Bi-LSTM, Bi-LSTM-CRF, IDCNN, IDCNN-CRF and BERT models on entity recognition. [Results] In the 10-fold cross validation, the highest F-score of the BERT model reached 81.39%, while its average F-score was 5.50% and 2.58% higher than those of IDCNN-CRF and Bi-LSTM-CRF models respectively. We built the integrated presentation platform for food safety incident entities based on the Bi-LSTM-CRF model. [Limitations] More research is needed to identify location entities from complex administrative regions. [Conclusions] The constructed platform supports policy formulation and food industry administration.