IDEAS home Printed from https://ideas.repec.org/a/oup/ijlctc/v20y2025ip626-634..html
   My bibliography  Save this article

Research on industry label recognition method based on RoBERTa driven by data

Author

Listed:
  • You Wen
  • Xuan Fan
  • Pingyan Mo

Abstract

This study introduces an enhanced RoBERTa-based model, called Industry Aware RoBERTa (IA-RoBERTa), designed to improve the accuracy and generalization of industry label recognition. IA-RoBERTa innovatively integrates structured industry knowledge through a knowledge graph fusion approach, using multigranularity input representation and industry-aware self-attention mechanisms. Together, these features enhance the model’s ability to efficiently process and understand industry-specific information. In addition, IA-RoBERTa includes a layered industry classifier that expertly handles fine-grained and layered industry categories. Experimental evaluations of industry label recognition datasets show that IA-RoBERTa outperforms existing methods in terms of accuracy, F1 scores, and macro-average performance metrics.

Suggested Citation

  • You Wen & Xuan Fan & Pingyan Mo, 2025. "Research on industry label recognition method based on RoBERTa driven by data," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 626-634.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:626-634.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf026
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:oup:ijlctc:v:20:y:2025:i::p:626-634.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/ijlct .

    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.