An Embedding Based IR Model for Disaster Situations

Ayan Bandyopadhyay (), Debasis Ganguly (), Mandar Mitra (), Sanjoy Kumar Saha () and Gareth J.F. Jones ()
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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

Information Systems Frontiers, 2018, vol. 20, issue 5, No 3, 925-932

Abstract: 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.

Keywords: Microblog; Twitter; Information retrieval; Word embedding (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10796-018-9847-6

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