An Integrated Platform for Food Safety Incident Entities Based on Deep Learning
Hu Haotian1,2,Ji Jinfeng3,Wang Dongbo3,4(),Deng Sanhong1,2
1School of Information Management, Nanjing University, Nanjing 210023, China 2Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China 3School of Information Management, Nanjing Agricultural University, Nanjing 210095, China 4Research Center for Correlation of Domain Knowledge, Nanjing Agricultural University,Nanjing 210095, China
[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.
胡昊天,吉晋锋,王东波,邓三鸿. 基于深度学习的食品安全事件实体一体化呈现平台构建*[J]. 数据分析与知识发现, 2021, 5(3): 12-24.
Hu Haotian,Ji Jinfeng,Wang Dongbo,Deng Sanhong. An Integrated Platform for Food Safety Incident Entities Based on Deep Learning. Data Analysis and Knowledge Discovery, 2021, 5(3): 12-24.
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