Application of AI Technology in Terminology Annotation

Yanxia Qin (), Zhijie Liu (), Ting Wang () and Jinwen Zhou ()
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Yanxia Qin: Xi’an Shiyou University
Zhijie Liu: Xi’an Shiyou University
Ting Wang: Xi’an Shiyou University
Jinwen Zhou: Xi’an Shiyou University

A chapter in Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025), 2026, pp 268-278 from Springer

Abstract: 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.

Keywords: Terminology Annotation; AI (Artificial Intelligence); Terminological Competence (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-602-9_26

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