Models and Techniques for Domain Relation Extraction: A Survey

Authors

  • Jiahui Wang School of Information Science and Engineering and Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, China https://orcid.org/0000-0001-6333-3230
  • Kun Yue School of Information Science and Engineering and Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, China https://orcid.org/0000-0003-3641-1461
  • Liang Duan School of Information Science and Engineering and Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, China https://orcid.org/0000-0001-9473-2533

DOI:

https://doi.org/10.47852/bonviewJDSIS3202973

Keywords:

relation extraction, domain knowledge, tasks, sentence-level models, document-level models, corpus

Abstract

As the significant subtask of information extraction, relation extraction (RE) aims to identify and classify semantic relations between pairs of entities and is widely adopted as the foundation of downstream applications including knowledge graphs, intelligent question answering, text mining, and sentiment analysis. Different from general knowledge, domain knowledge is pertinent to specific fields which include a wealth of proprietary entities and relations. Besides, most of the data are formed as documents rather than sentences. In this paper, the task of domain RE is defined, and the common domains are presented. Furthermore, we provide a systematic review of state-of-the-art techniques as well as the latest trends. We survey different neural network-based techniques for RE and describe the overall framework, training procedures, as well as the pros and cons of these techniques. Then, we introduce and compare the corpus and metrics used for domain RE tasks. Finally, we conclude and discuss future research issues of domain RE.

 

Received: 17 April 2023 | Revised: 29 May 2023 | Accepted: 31 May 2023

 

Conflicts of Interest:

The authors declare that they have no conflicts of interest to this work.


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Published

2023-05-31

How to Cite

Wang, J., Yue, K., & Duan, L. (2023). Models and Techniques for Domain Relation Extraction: A Survey. Journal of Data Science and Intelligent Systems, 1(2), 65–82. https://doi.org/10.47852/bonviewJDSIS3202973

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Section

Review

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