IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v9y2017i4p340-360.html
   My bibliography  Save this article

A lexical-semantics-based method for multi label text categorisation using word net

Author

Listed:
  • Rajni Jindal
  • Shweta Taneja

Abstract

Text categorisation is an upcoming area in the field of text mining. The text documents possess huge number of features due to their unstructured nature. In this paper, an algorithm for multi label categorisation of text documents based on the concepts of lexical and semantics using word net (MC-LSW) is proposed. The proposed algorithm is based on the concepts of lexical (tokens) and semantics of a language. It aims at minimising the number of tokens used for categorising text documents. MC-LSW uses word net to extract the semantic information of tokens. The proposed algorithm is implemented and tested on five datasets of text domain and is compared with the existing multi label categorisation algorithms. The proposed algorithm (MC-LSW) shows more efficient and promising results in terms of space and time complexity than the existing methods. Accuracy and precision measures have been improved by the proposed algorithm as well as hamming loss has been reduced.

Suggested Citation

  • Rajni Jindal & Shweta Taneja, 2017. "A lexical-semantics-based method for multi label text categorisation using word net," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 9(4), pages 340-360.
    • Handle: RePEc:ids:ijdmmm:v:9:y:2017:i:4:p:340-360
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=88412
    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.

    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:ijdmmm:v:9:y:2017:i:4:p:340-360. 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=342 .

    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.