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        <title>Disaster Analysis using User-Generated Weather Report</title>
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    <name type="personal">
        <namePart type="given">Yasunobu</namePart>
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    <name type="personal">
        <namePart type="given">Masatsugu</namePart>
        <namePart type="family">Hangyo</namePart>
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    <name type="personal">
        <namePart type="given">Mamoru</namePart>
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            <title>Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)</title>
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        <name type="personal">
            <namePart type="given">Bo</namePart>
            <namePart type="family">Han</namePart>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Tim</namePart>
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            <publisher>The COLING 2016 Organizing Committee</publisher>
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    <abstract>Information extraction from user-generated text has gained much attention with the growth of the Web.Disaster analysis using information from social media provides valuable, real-time, geolocation information for helping people caught up these in disasters. However, it is not convenient to analyze texts posted on social media because disaster keywords match any texts that contain words. For collecting posts about a disaster from social media, we need to develop a classifier to filter posts irrelevant to disasters. Moreover, because of the nature of social media, we can take advantage of posts that come with GPS information. However, a post does not always refer to an event occurring at the place where it has been posted. Therefore, we propose a new task of classifying whether a flood disaster occurred, in addition to predicting the geolocation of events from user-generated text. We report the annotation of the flood disaster corpus and develop a classifier to demonstrate the use of this corpus for disaster analysis.</abstract>
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%0 Conference Proceedings
%T Disaster Analysis using User-Generated Weather Report
%A Asakura, Yasunobu
%A Hangyo, Masatsugu
%A Komachi, Mamoru
%Y Han, Bo
%Y Ritter, Alan
%Y Derczynski, Leon
%Y Xu, Wei
%Y Baldwin, Tim
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F asakura-etal-2016-disaster
%X Information extraction from user-generated text has gained much attention with the growth of the Web.Disaster analysis using information from social media provides valuable, real-time, geolocation information for helping people caught up these in disasters. However, it is not convenient to analyze texts posted on social media because disaster keywords match any texts that contain words. For collecting posts about a disaster from social media, we need to develop a classifier to filter posts irrelevant to disasters. Moreover, because of the nature of social media, we can take advantage of posts that come with GPS information. However, a post does not always refer to an event occurring at the place where it has been posted. Therefore, we propose a new task of classifying whether a flood disaster occurred, in addition to predicting the geolocation of events from user-generated text. We report the annotation of the flood disaster corpus and develop a classifier to demonstrate the use of this corpus for disaster analysis.
%U https://aclanthology.org/W16-3906
%P 24-32

Markdown (Informal)

[Disaster Analysis using User-Generated Weather Report](https://aclanthology.org/W16-3906) (Asakura et al., WNUT 2016)

• Disaster Analysis using User-Generated Weather Report (Asakura et al., WNUT 2016)

ACL

• Yasunobu Asakura, Masatsugu Hangyo, and Mamoru Komachi. 2016. Disaster Analysis using User-Generated Weather Report. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 24–32, Osaka, Japan. The COLING 2016 Organizing Committee.