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    <titleInfo>
        <title>Building timelines of soccer matches from Twitter</title>
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    <name type="personal">
        <namePart type="given">Amosse</namePart>
        <namePart type="family">Edouard</namePart>
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    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="family">Cabrio</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Sara</namePart>
        <namePart type="family">Tonelli</namePart>
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    <name type="personal">
        <namePart type="given">Nhan</namePart>
        <namePart type="family">Le-Thanh</namePart>
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        <dateIssued>2017-09</dateIssued>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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            <publisher>INCOMA Ltd.</publisher>
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    <abstract>This demo paper presents a system that builds a timeline with salient actions of a soccer game, based on the tweets posted by users. It combines information provided by external knowledge bases to enrich the content of tweets and applies graph theory to model relations between actions (e.g. goals, penalties) and participants of a game (e.g. players, teams). In the demo, a web application displays in nearly real-time the actions detected from tweets posted by users for a given match of Euro 2016. Our tools are freely available at https://bitbucket.org/eamosse/event_tracking.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_029</identifier>
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%0 Conference Proceedings
%T Building timelines of soccer matches from Twitter
%A Edouard, Amosse
%A Cabrio, Elena
%A Tonelli, Sara
%A Le-Thanh, Nhan
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F edouard-etal-2017-building
%X This demo paper presents a system that builds a timeline with salient actions of a soccer game, based on the tweets posted by users. It combines information provided by external knowledge bases to enrich the content of tweets and applies graph theory to model relations between actions (e.g. goals, penalties) and participants of a game (e.g. players, teams). In the demo, a web application displays in nearly real-time the actions detected from tweets posted by users for a given match of Euro 2016. Our tools are freely available at https://bitbucket.org/eamosse/event_tracking.
%R 10.26615/978-954-452-049-6_029
%U https://doi.org/10.26615/978-954-452-049-6_029
%P 208-213

Markdown (Informal)

[Building timelines of soccer matches from Twitter](https://doi.org/10.26615/978-954-452-049-6_029) (Edouard et al., RANLP 2017)

• Building timelines of soccer matches from Twitter (Edouard et al., RANLP 2017)

ACL

• Amosse Edouard, Elena Cabrio, Sara Tonelli, and Nhan Le-Thanh. 2017. Building timelines of soccer matches from Twitter. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 208–213, Varna, Bulgaria. INCOMA Ltd..