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        <title>Classification of Tweets about Reported Events using Neural Networks</title>
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        <namePart type="given">Kiminobu</namePart>
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        <namePart type="given">Yuka</namePart>
        <namePart type="family">Takei</namePart>
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    <name type="personal">
        <namePart type="given">Taro</namePart>
        <namePart type="family">Miyazaki</namePart>
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    <name type="personal">
        <namePart type="given">Jun</namePart>
        <namePart type="family">Goto</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
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    <abstract>We developed a system that automatically extracts “Event-describing Tweets” which include incidents or accidents information for creating news reports. Event-describing Tweets can be classified into “Reported-event Tweets” and “New-information Tweets.” Reported-event Tweets cite news agencies or user generated content sites, and New-information Tweets are other Event-describing Tweets. A system is needed to classify them so that creators of factual TV programs can use them in their productions. Proposing this Tweet classification task is one of the contributions of this paper, because no prior papers have used the same task even though program creators and other events information collectors have to do it to extract required information from social networking sites. To classify Tweets in this task, this paper proposes a method to input and concatenate character and word sequences in Japanese Tweets by using convolutional neural networks. This proposed method is another contribution of this paper. For comparison, character or word input methods and other neural networks are also used. Results show that a system using the proposed method and architectures can classify Tweets with an F1 score of 88 %.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6121</identifier>
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        <date>2018-11</date>
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%0 Conference Proceedings
%T Classification of Tweets about Reported Events using Neural Networks
%A Makino, Kiminobu
%A Takei, Yuka
%A Miyazaki, Taro
%A Goto, Jun
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F makino-etal-2018-classification
%X We developed a system that automatically extracts “Event-describing Tweets” which include incidents or accidents information for creating news reports. Event-describing Tweets can be classified into “Reported-event Tweets” and “New-information Tweets.” Reported-event Tweets cite news agencies or user generated content sites, and New-information Tweets are other Event-describing Tweets. A system is needed to classify them so that creators of factual TV programs can use them in their productions. Proposing this Tweet classification task is one of the contributions of this paper, because no prior papers have used the same task even though program creators and other events information collectors have to do it to extract required information from social networking sites. To classify Tweets in this task, this paper proposes a method to input and concatenate character and word sequences in Japanese Tweets by using convolutional neural networks. This proposed method is another contribution of this paper. For comparison, character or word input methods and other neural networks are also used. Results show that a system using the proposed method and architectures can classify Tweets with an F1 score of 88 %.
%R 10.18653/v1/W18-6121
%U https://aclanthology.org/W18-6121
%U https://doi.org/10.18653/v1/W18-6121
%P 153-163

Markdown (Informal)

[Classification of Tweets about Reported Events using Neural Networks](https://aclanthology.org/W18-6121) (Makino et al., WNUT 2018)

• Classification of Tweets about Reported Events using Neural Networks (Makino et al., WNUT 2018)

ACL

• Kiminobu Makino, Yuka Takei, Taro Miyazaki, and Jun Goto. 2018. Classification of Tweets about Reported Events using Neural Networks. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 153–163, Brussels, Belgium. Association for Computational Linguistics.