IDEAS home Printed from https://ideas.repec.org/a/ids/ijbisy/v28y2018i1p79-94.html
   My bibliography  Save this article

Multi term based co-term frequency method for term weighting in information retrieval

Author

Listed:
  • M. Santhanakumar
  • C. Christopher Columbus
  • K. Jayapriya

Abstract

Nowadays, World Wide Web (WWW) has become the only source of all kind of information. Retrieving the relevant web pages based on user queries from WWW is an exigent task. Term frequency inverse document frequency (TF-IDF) is the most frequently used method for term weighting based on the occurrences and presence of a term inside the document. Retrieved document based on a single query term may not relate to the user search. This may lead the user to process the unwanted information. So, this paper proposes a new term weighting method named co-term frequency, in which the weight is assigned according to the multi terms which commonly occur in all documents. From the measures of precision, recall and F-score of the proposed method, it is clearly evident that the proposed framework retrieves the most relevant web pages when compared to other term weighting methods.

Suggested Citation

  • M. Santhanakumar & C. Christopher Columbus & K. Jayapriya, 2018. "Multi term based co-term frequency method for term weighting in information retrieval," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 28(1), pages 79-94.
    • Handle: RePEc:ids:ijbisy:v:28:y:2018:i:1:p:79-94
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=91164
    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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dogan, Turgut & Uysal, Alper Kursat, 2020. "A novel term weighting scheme for text classification: TF-MONO," Journal of Informetrics, Elsevier, vol. 14(4).

    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:ids:ijbisy:v:28:y:2018:i:1:p:79-94. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=172 .

    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.