IDEAS home Printed from https://ideas.repec.org/a/ijs/ijsrse/v2y2016i2idhijsrset1622398.html

A Review of Relation Classification with Convolutional Neural Network

Author

Listed:
  • Kartik Dhiwar
  • Abhishek Kumar Dewangan

Abstract

Relation classification is one of the important research issue in the field of Natural Language Processing (NLP). It is a crucial intermediate step in complex knowledge intensive applications like automatic knowledgebase construction, question answering, textual entailment, search engine etc. Recently neural network has given state of art results in various relation extraction tasks without depending much on manually engineered features. In this paper we present brief review on different model that has been proposed for relation classification and compare their results.

Suggested Citation

  • Kartik Dhiwar & Abhishek Kumar Dewangan, 2016. "A Review of Relation Classification with Convolutional Neural Network," International Journal of Scientific Research in Science, Engineering and Technology, International Journal of Scientific Research in Science, Engineering and Technology, vol. 2(2), pages 1167-1171, December.
  • Handle: RePEc:ijs:ijsrse:v2:y2016:i2:id:hijsrset1622398
    Note: Article URL: https://ijsrset.com/IJSRSET1622398
    as

    Download full text from publisher

    File URL: https://ijsrset.com/IJSRSET1622398
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrset.com/paper/1259.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    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:ijs:ijsrse:v2:y2016:i2:id:hijsrset1622398. 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: Pankaj Sharma (email available below). General contact details of provider: https://ijsrset.com/home .

    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.