Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)

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Application of AI Technology in Terminology Annotation

Authors
Yanxia Qin1, *, Zhijie Liu1, Ting Wang1, Jinwen Zhou1
1Xi’an Shiyou University, Xi’an, Shaanxi Province, 710065, China
*Corresponding author. Email: yxqin@xsyu.edu.cn
Corresponding Author
Yanxia Qin
Available Online 13 March 2026.
DOI
10.2991/978-94-6239-602-9_26How to use a DOI?
Keywords
Terminology Annotation; AI (Artificial Intelligence); Terminological Competence
Abstract

Accurate terminology annotation is crucial for translators to enhance text processing efficiency and ensure translation quality control. Within the translation workflow, it stands as the most critical step in the pre-translation phase. Terminological competence is an essential “bread-and-butter skill” for professional translators. Traditional terminology annotation methods, reliant on manual experience or rule-based technologies, often suffer from low efficiency and poor accuracy. This teaching case study utilizes petroleum science and technology literature as the source text for terminology annotation activity. It focuses on AI-empowered terminology annotation within the translation process, demonstrating how a “teacher-student-machine synergy” adds powerful wings to annotation efficiency. The implementation involves establishing a manual annotation group, and an upgraded human-machine collaborative annotation group. This framework guides students to deeply explore AI-augmented annotation, cultivates their ability for human-machine collaborative and co-creation, and allows them to experience the satisfaction of human-machine win-win outcomes. The ultimate goal is to achieve the practical teaching objective of enhancing students’ terminological competence.

Copyright
© 2026 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.

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Volume Title
Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
13 March 2026
ISBN
978-94-6239-602-9
ISSN
2352-5428
DOI
10.2991/978-94-6239-602-9_26How to use a DOI?
Copyright
© 2026 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  - Yanxia Qin
AU  - Zhijie Liu
AU  - Ting Wang
AU  - Jinwen Zhou
PY  - 2026
DA  - 2026/03/13
TI  - Application of AI Technology in Terminology Annotation
BT  - Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)
PB  - Atlantis Press
SP  - 268
EP  - 278
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-602-9_26
DO  - 10.2991/978-94-6239-602-9_26
ID  - Qin2026
ER  -