Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023)

<Previous Article In Volume
Next Article In Volume>

A Survey on Document-Level Relation Extraction: Methods and Applications

Authors
Yifan Zheng1, *, Yikai Guo1, Zhizhao Luo1, Zengwen Yu1, Kunlong Wang1, Hong Zhang1, Hua Zhao1
1Beijing Institute of Computer Technology and Application, Beijing, China
*Corresponding author. Email: zhengyifan_ht@163.com
Corresponding Author
Yifan Zheng
Available Online 4 September 2023.
DOI
10.2991/978-94-6463-230-9_128How to use a DOI?
Keywords
information extraction; document-level relation extraction; application
Abstract

Relation extraction is a significant area of research in the field of information extraction, to extract target information accurately and efficiently from vast amounts of data to improve the utilization of information. Relation extraction is widely used in various downstream tasks such as text mining, information retrieval, and question-answering systems. Compared to sentence-level relation extraction, document-level relation extraction is more complex and challenging, yet there is a lack of a comprehensive overview of document-level relation extraction. This paper presents a survey on document-level relation extraction, first categorizing existing techniques into three categories and introducing the most representative models. Then, we describe the primary application domains and commonly used datasets for relation extraction. Finally, we analyse the research challenges and future trends in document-level relation extraction.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
4 September 2023
ISBN
10.2991/978-94-6463-230-9_128
ISSN
2667-128X
DOI
10.2991/978-94-6463-230-9_128How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yifan Zheng
AU  - Yikai Guo
AU  - Zhizhao Luo
AU  - Zengwen Yu
AU  - Kunlong Wang
AU  - Hong Zhang
AU  - Hua Zhao
PY  - 2023
DA  - 2023/09/04
TI  - A Survey on Document-Level Relation Extraction: Methods and Applications
BT  - Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023)
PB  - Atlantis Press
SP  - 1061
EP  - 1071
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-230-9_128
DO  - 10.2991/978-94-6463-230-9_128
ID  - Zheng2023
ER  -