IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6239-602-9_26.html

Application of AI Technology in Terminology Annotation

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

Author

Listed:
  • Yanxia Qin

    (Xi’an Shiyou University)

  • Zhijie Liu

    (Xi’an Shiyou University)

  • Ting Wang

    (Xi’an Shiyou University)

  • Jinwen Zhou

    (Xi’an Shiyou University)

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.

Suggested Citation

  • Yanxia Qin & Zhijie Liu & Ting Wang & Jinwen Zhou, 2026. "Application of AI Technology in Terminology Annotation," Advances in Economics, Business and Management Research, in: Touria Benazzouz & Sandeep Saxena & Hui Nee Au Yong & Nor Zafir Md Salleh (ed.), Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025), pages 268-278, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-602-9_26
    DOI: 10.2991/978-94-6239-602-9_26
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advbcp:978-94-6239-602-9_26. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.