IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/s9bza.html
   My bibliography  Save this paper

Building Intelligent Chatbot Systems using Meta-Analysis and Deep Learning

Author

Listed:
  • Jsowd, Kyldo

Abstract

Chatbot systems have gained significant attention in recent years due to their potential to automate customer interactions and provide personalized assistance. This article presents a novel approach for building intelligent chatbot systems by leveraging the power of meta-analysis and deep learning techniques. In this study, we propose a framework that combines meta-analysis, which synthesizes findings from existing chatbot research, with deep learning algorithms to enhance the performance and intelligence of chatbot systems. We explore the application of deep learning models, such as recurrent neural networks (RNNs) and transformer models, for various chatbot tasks, including natural language understanding, dialogue management, and response generation

Suggested Citation

  • Jsowd, Kyldo, 2023. "Building Intelligent Chatbot Systems using Meta-Analysis and Deep Learning," OSF Preprints s9bza, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:s9bza
    DOI: 10.31219/osf.io/s9bza
    as

    Download full text from publisher

    File URL: https://osf.io/download/64b422596f8ad00313bc1d7f/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/s9bza?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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:osf:osfxxx:s9bza. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.