IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v20y2018i5d10.1007_s10796-018-9847-6.html
   My bibliography  Save this article

An Embedding Based IR Model for Disaster Situations

Author

Listed:
  • Ayan Bandyopadhyay

    (Indian Statistical Institute)

  • Debasis Ganguly

    (IBM Research)

  • Mandar Mitra

    (Indian Statistical Institute)

  • Sanjoy Kumar Saha

    (Jadavpur University)

  • Gareth J.F. Jones

    (Dublin City University)

Abstract

Twitter ( http://twitter.com ) is one of the most popular social networking platforms. Twitter users can easily broadcast disaster-specific information, which, if effectively mined, can assist in relief operations. However, the brevity and informal nature of tweets pose a challenge to Information Retrieval (IR) researchers. In this paper, we successfully use word embedding techniques to improve ranking for ad-hoc queries on microblog data. Our experiments with the ‘Social Media for Emergency Relief and Preparedness’ (SMERP) dataset provided at an ECIR 2017 workshop show that these techniques outperform conventional term-matching based IR models. In addition, we show that, for the SMERP task, our word embedding based method is more effective if the embeddings are generated from the disaster specific SMERP data, than when they are trained on the large social media collection provided for the TREC ( http://trec.nist.gov/ ) 2011 Microblog track dataset.

Suggested Citation

  • Ayan Bandyopadhyay & Debasis Ganguly & Mandar Mitra & Sanjoy Kumar Saha & Gareth J.F. Jones, 2018. "An Embedding Based IR Model for Disaster Situations," Information Systems Frontiers, Springer, vol. 20(5), pages 925-932, October.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:5:d:10.1007_s10796-018-9847-6
    DOI: 10.1007/s10796-018-9847-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-018-9847-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-018-9847-6?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
    ---><---

    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. Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
    2. Abhinav Kumar & Jyoti Prakash Singh & Nripendra P. Rana & Yogesh K. Dwivedi, 2023. "Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster," Information Systems Frontiers, Springer, vol. 25(4), pages 1589-1604, August.
    3. Yanxin Wang & Jian Li & Xi Zhao & Gengzhong Feng & Xin (Robert) Luo, 2020. "Using Mobile Phone Data for Emergency Management: a Systematic Literature Review," Information Systems Frontiers, Springer, vol. 22(6), pages 1539-1559, December.
    4. Saptarshi Ghosh & Kripabandhu Ghosh & Debasis Ganguly & Tanmoy Chakraborty & Gareth J. F. Jones & Marie-Francine Moens & Muhammad Imran, 2018. "Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(5), pages 901-907, October.

    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:spr:infosf:v:20:y:2018:i:5:d:10.1007_s10796-018-9847-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.