IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v19y2025i1s1751157724001196.html
   My bibliography  Save this article

Citation recommendation based on argumentative zoning of user queries

Author

Listed:
  • Ma, Shutian
  • Zhang, Chengzhi
  • Zhang, Heng
  • Gao, Zheng

Abstract

Citation recommendation aims to locate the important papers for scholars to cite. When writing the citing sentences, the authors usually hold different citing intents, which are referred to citation function in citation analysis. Since argumentative zoning is to identify the argumentative and rhetorical structure in scientific literature, we want to use this information to improve the citation recommendation task. In this paper, a multi-task learning model is built for citation recommendation and argumentative zoning classification. We also generated an annotated corpus of the data from PubMed Central based on a new argumentative zoning schema. The experimental results show that, by considering the argumentative information in the citing sentence, citation recommendation model will get better performance.

Suggested Citation

  • Ma, Shutian & Zhang, Chengzhi & Zhang, Heng & Gao, Zheng, 2025. "Citation recommendation based on argumentative zoning of user queries," Journal of Informetrics, Elsevier, vol. 19(1).
    • Handle: RePEc:eee:infome:v:19:y:2025:i:1:s1751157724001196
    DOI: 10.1016/j.joi.2024.101607
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157724001196
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2024.101607?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:eee:infome:v:19:y:2025:i:1:s1751157724001196. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.