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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (3): 12-24    DOI: 10.11925/infotech.2096-3467.2019.1031
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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
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Abstract  

[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.

Key wordsDeep Learning      Food Safety Incident Entity      Bi-LSTM-CRF      BERT     
Received: 11 September 2019      Published: 12 April 2021
ZTFLH:  G255  
Fund:Philosophy and Social Science Research Fund of Jiangsu Education Department and Central University Fund of Nanjing Agricultural University(2018SJA0034);National Social Science Fund of China(15ZDB168);Hubei Collaborative Innovation Center(JD20150101)
Corresponding Authors: Wang Dongbo     E-mail: db.wang@njau.edu.cn

Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1031     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I3/12

Manual Labeling of Food Safety Incident Entities
The Architecture of CRF Model
The Architecture of Bi-LSTM-CRF Model
The Architecture of BERT Model
Flow Chart of the Entity Recognition Experiment
标记 含义
B-fd 食品与诱因实体起始字
I-fd 食品与诱因实体中间字
E-fd 食品与诱因实体结束字
B-ot 时间与地点实体起始字
I-ot 时间与地点实体中间字
E-ot 时间与地点实体结束字
O 非食品安全事件实体字
Tags and Their Meanings
序号 准确率 召回率 F值
1 73.65% 77.58% 75.57%
2 74.03% 77.82% 75.88%
3 73.25% 77.50% 75.31%
4 73.75% 77.34% 75.50%
5 71.78% 77.42% 74.49%
6 71.86% 77.77% 74.70%
7 72.50% 77.44% 74.89%
8 72.88% 79.02% 75.83%
9 72.98% 78.96% 75.85%
10 71.15% 79.49% 75.09%
平均值 72.78% 78.03% 75.31%
10-fold Cross-validation of Food Safety Incident Entity Recognition Based on IDCNN-CRF
标记 准确率 召回率 F值
fd 75.56% 79.30% 77.38%
ot 70.56% 74.45% 72.45%
全部 74.03% 77.82% 75.88%
Entity Recognition Result of the Best Performing IDCNN-CRF
序号 准确率 召回率 F值
1 73.92% 80.34% 77.00%
2 76.60% 81.08% 78.78%
3 74.60% 82.09% 78.17%
4 76.15% 81.24% 78.61%
5 75.98% 79.70% 77.79%
6 76.24% 79.94% 78.05%
7 74.38% 81.85% 77.94%
8 76.98% 81.57% 79.21%
9 74.82% 81.55% 78.04%
10 75.43% 82.31% 78.72%
平均值 75.51% 81.17% 78.23%
10-fold Cross-validation of Food Safety Incident Entity Recognition Based on Bi-LSTM-CRF
标记 准确率 召回率 F值
fd 77.02% 82.87% 79.84%
ot 76.92% 79.62% 78.25%
全部 76.98% 81.57% 79.21%
Entity Recognition Result of the Best Performing Bi-LSTM-CRF
序号 准确率 召回率 F值
1 77.46% 82.85% 80.06%
2 78.89% 82.96% 80.87%
3 77.79% 83.91% 80.74%
4 78.21% 84.30% 81.14%
5 77.80% 83.00% 80.32%
6 78.52% 83.75% 81.05%
7 77.42% 83.11% 80.16%
8 79.28% 83.61% 81.39%
9 78.18% 84.04% 81.00%
10 78.64% 84.25% 81.35%
平均值 78.22% 83.58% 80.81%
10-fold Cross-validation of Food Safety Incident Entity Recognition Based on BERT
标记 准确率 召回率 F值
fd 81.71% 85.23% 83.44%
ot 75.65% 81.13% 78.29%
全部 79.28% 83.61% 81.39%
Entity Recognition Result of the Best Performing BERT
模型 准确率 召回率 F值
IDCNN 59.20% 74.55% 65.99%
IDCNN-CRF 72.78% 78.03% 75.31%
Bi-LSTM 54.08% 78.83% 64.15%
Bi-LSTM-CRF 75.51% 81.17% 78.23%
BERT 78.22% 83.58% 80.81%
Comparison of Recognition Effects
Distribution of China's Food Safety Incidents in Different Provinces from 2007 to 2017
Distribution of China’s Food Safety Incidents from 2007 to 2017
Monthly Distribution of Food Safety Incidents in China from 2007 to 2017
Food and Inducement Entities
Time and Place Entities
Screenshot of API Call Interface
Screenshot of the Database Interface
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