An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain.

Journal: Computational intelligence and neuroscience
Published Date: Feb 21, 2022

Abstract

In recent years, entity relation extraction has been a critical technique to help people analyze complex structured text data. However, there is no advanced research in food health and safety to help people analyze the complex concepts between food and human health and their relationships. This paper proposes an entity relation extraction method FHER for the few-shot learning in the food health and safety domain. For few-shot learning in the food health and safety domain, we propose three methods that effectively improve the performance of entity relationship extraction. The three methods are applied to the self-built data sets FH and MHD. The experimental results show that the method can effectively extract domain-related entities and their relations in a small sample size environment.

Authors

  • Min Zuo
    School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China. zuomin@btbu.edu.cn.
  • Baoyu Zhang
    Beijing Technology and Business University, National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100048, China.
  • Qingchuan Zhang
    National Engineering Research Centre for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.
  • Wenjing Yan
    Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing, China.
  • Dongmei Ai
    Basic Experimental Center of Natural Science, University of Science and Technology Beijing, Beijing 100083, P. R. China.

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