IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-96-9697-0_93.html
   My bibliography  Save this book chapter

An Entity Recognition Model for Vulnerability Knowledge Domain with Chinese and English Text

Author

Listed:
  • Li Zhang

    (Beijing Jiaotong University)

  • Jianqin Zhou

    (Beijing Jiaotong University)

Abstract

In order to develop knowledge graphs to help experts in the vulnerability industry to quickly grasp vulnerability information, we need an entity recognition model to automatically extract entities in the vulnerability domain from massive text data. Considering that entities in the vulnerability domain are a mixture of Chinese and English, this paper proposes an entity recognition model, ERNIE + BILSTM + CRF, which combines the ERNIE pre-training model with the BILSTM and CRF models to enhance the extraction of semantic information from Chinese text. Experimental results show the effectiveness efficiency of the model in entity recognition for both Chinese and English texts.

Suggested Citation

  • Li Zhang & Jianqin Zhou, 2025. "An Entity Recognition Model for Vulnerability Knowledge Domain with Chinese and English Text," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-981-96-9697-0_93
    DOI: 10.1007/978-981-96-9697-0_93
    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:lnopch:978-981-96-9697-0_93. 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.