An Embedding Based IR Model for Disaster Situations
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 Re...
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| Veröffentlicht in: | Information systems frontiers 2018-10, Vol.20 (5), p.925-932 |
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| creator | Bandyopadhyay, Ayan Ganguly, Debasis Mitra, Mandar Saha, Sanjoy Kumar Jones, Gareth J.F. |
| description | 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. |
| doi_str_mv | 10.1007/s10796-018-9847-6 |
| format | Article |
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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/
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http://trec.nist.gov/
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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.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10796-018-9847-6</doi><tpages>8</tpages></addata></record> |
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| subjects | Business and Management Control Digital media Disaster management Embedding Emergency management Emergency preparedness Information retrieval Information systems IT in Business Management of Computing and Information Systems Operations Research/Decision Theory Social networks Systems Theory |
| title | An Embedding Based IR Model for Disaster Situations |
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