IDEAS home Printed from https://ideas.repec.org/a/alk/ijllt0/v8y2025i2id8784.html

Automatic Discovery of Nativelike Selections through Topic Modeling: an Experiment Based on Online Reviews

Author

Listed:
  • Huasheng Zhang

    (College of Liberal Arts, Sichuan Normal University, China)

Abstract

Nativelike selections are linguistic expressions favored by native speakers over other grammatically correct alternatives to express a certain concept. They are important items for language teaching and lexicography. This study proposes an automatic method for discovering them through the application of the topic modeling technique of BERTopic which discovers the frequent linguistic expressions that express the same topic. The method’s effectiveness and efficiency are demonstrated through an experiment based on online reviews of a dish soap product in English, Chinese, and Japanese.

Suggested Citation

  • Huasheng Zhang, 2025. "Automatic Discovery of Nativelike Selections through Topic Modeling: an Experiment Based on Online Reviews," International Journal of Linguistics, Literature and Translation, Al-Kindi Center for Research and Development, vol. 8(2), pages 101-104, February.
  • Handle: RePEc:alk:ijllt0:v:8:y:2025:i:2:id:8784
    DOI: 10.32996/ijllt.2025.8.2.11
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/ijllt/article/view/8784
    File Function: Abstract page
    Download Restriction: no

    File URL: https://al-kindipublisher.com/index.php/ijllt/article/download/8784/7468
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32996/ijllt.2025.8.2.11?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
    ---><---

    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:alk:ijllt0:v:8:y:2025:i:2:id:8784. 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: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/ijllt .

    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.