IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v13y2017i2p1550147717694891.html
   My bibliography  Save this article

A study on query terms proximity embedding for information retrieval

Author

Listed:
  • Ya-nan Qiao
  • Qinghe Du
  • Di-fang Wan

Abstract

Information retrieval is applied widely to models and algorithms in wireless networks for cyber-physical systems. Query terms proximity has proved that it is a very useful information to improve the performance of information retrieval systems. Query terms proximity cannot retrieve documents independently, and it must be incorporated into original information retrieval models. This article proposes the concept of query term proximity embedding, which is a new method to incorporate query term proximity into original information retrieval models. Moreover, term-field-convolutions frequency framework, which is an implementation of query term proximity embedding, is proposed in this article, and experimental results show that this framework can improve the performance effectively compared with traditional proximity retrieval models.

Suggested Citation

  • Ya-nan Qiao & Qinghe Du & Di-fang Wan, 2017. "A study on query terms proximity embedding for information retrieval," International Journal of Distributed Sensor Networks, , vol. 13(2), pages 15501477176, February.
    • Handle: RePEc:sae:intdis:v:13:y:2017:i:2:p:1550147717694891
    DOI: 10.1177/1550147717694891
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147717694891
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147717694891?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
    ---><---

    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:sae:intdis:v:13:y:2017:i:2:p:1550147717694891. 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: SAGE Publications (email available below). General contact details of provider: .

    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.