Extracting Events of Food Safety Emergencies with Characteristics Knowledge
Wang Dongbo1,2(), Wu Yi1, Ye Wenhao1, Liu Ruilun1
1College of Information and Technology, Nanjing Agricultural University, Nanjing 210095, China 2Research Center for Correlation of Domain Knowledge, Nanjing Agricultural University, Nanjing 210095, China
[Objective] This paper aims to extract the events of food safety emergencies from large food safety emergencies. [Methods] First, we built the food safety emergency corpus based on the past events, as well as the data acquisition, labeling, and organization methods of information science. Then, we extracted the corresponding events with the help of conditional random field model, and the distribution characteristics knowledge of the food safety emergencies. [Limitations] We might not be able to apply the feature template created by this research to other fields. [Results] We examined the proposed model with a food safety emergency corpus of 15 million Chinese words, and the F value of this model reached 91.94%. [Conclusions] It is feasible for us to extract the events from food safety emergency corpus with the help of conditional random field model.
(Zhang Mujie, Shen Jianhua.About the Disposal of the Food and Drug Safety Incident Information to the Public Thinking about the Disposal of the Food and Drug Safety Incident Information[J]. Shanghai Food and Drug Information Research, 2012(2): 45-49.)
(Ma Ying, Zhang Yuanyuan, Song Wenguang.Research on Epidemic Model of Emergency Events Risk Perception in Food Industry[J]. Science Research Management, 2013, 34(9): 123-130.)
(Chen Yu, Zheng Dequan,Zhao Tiejun.Chinese Relation Extraction Based on Deep Belief Nets[J]. Journal of Software, 2012, 23(10): 2572-2585.)
doi: 10.3724/SP.J.1001.2012.04181
(Xu Hua, Liu Maofu, Jiang Li, et al.Disease and Bacteria Entity Extraction Based on Linguistic Rule[J].Journal of Wuhan University: Natural Science Edition, 2015, 61(2): 51-55.)
doi: 10.14188/j.1671-8836.2015.02.008
[7]
魏秀卓. 食品投诉文本敏感词汇抽取研究[D]. 长春: 东北师范大学, 2015.
[7]
(Wei Xiuzhuo.Food Complaint Text Sensitive Words Extraction Research [D]. Changchun: Northeast Normal University, 2015.)
[8]
高蕊. 基于本体的食品投诉文本危害信息抽取研究[D]. 长春: 东北师范大学, 2011.
[8]
(Gao Rui.Ontology-based Hazard Information Extraction from Chinese Food Complaint Documents[D]. Changchun: Northeast Normal University, 2011.)
(Li Lishuang, Dang Yanzhong, Zhang Jing, et al.Automotive Term Extraction Based on Conditional Random Fields[J]. Journal of Dalian University of Technology, 2013, 53(2): 267-272.)
doi: 10.7511/dllgxb201302018
(Wang Longwen, Wang Dongbo.Project Application-oriented Named Entity Extraction Model Construction[J]. Information and Documentation Services, 2015(1): 30-34.)
doi: 10.3969/j.issn.1002-0314.2015.01.005
(Liu Kai, Zhou Xuezhong, Yu Jian, et al.Named Entity Extraction of Traditional Chinese Medicine Medical Records Based on Conditional Random Field[J]. Computer Engineering, 2014, 40(9): 312-316.)
doi: 10.3969/j.issn.1000-3428.2014.09.062
[12]
吴云芳. 面向语言信息处理的现代汉语并列结构研究[M]. 北京: 北京师范大学出版社, 2004.
[12]
(Wu Yunfang.Researches of Modern Chinese Coordinate Construction for Language Information Processing[M]. Beijing: Beijing Normal University Press, 2004.)
[13]
Lafferty J, McCallum A, Pereira F. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data[C]// Proceedings of the 18th International Conference on Machine Learning. 2001: 282-289.
[14]
McCallum A, Freitag D, Pereira F. Maximum Entropy Markov Models for Information Extraction and Segmentation[C]//Proceedings of the 17th International Conference on Machine Learning. 2000: 591-598.