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Information Systems Frontiers

Erschienen in:

04.04.2018

An Embedding Based IR Model for Disaster Situations

verfasst von: Ayan Bandyopadhyay, Debasis Ganguly, Mandar Mitra, Sanjoy Kumar Saha, Gareth J.F. Jones

Erschienen in: Information Systems Frontiers | Ausgabe 5/2018

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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.

Vorheriger Artikel Emergency Vocabulary

Nächster Artikel Classifying and Summarizing Information from Microblogs During Epidemics

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Titel: An Embedding Based IR Model for Disaster Situations
verfasst von: Ayan Bandyopadhyay
Debasis Ganguly
Mandar Mitra
Sanjoy Kumar Saha
Gareth J.F. Jones
Publikationsdatum: 04.04.2018
Verlag: Springer US
Erschienen in: Information Systems Frontiers / Ausgabe 5/2018
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-018-9847-6

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