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        <title>An Ensemble Approach for Aggression Identification in English and Hindi Text</title>
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        <namePart type="given">Arjun</namePart>
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        <namePart type="given">Prashant</namePart>
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            <title>Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)</title>
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        <name type="personal">
            <namePart type="given">Ritesh</namePart>
            <namePart type="family">Kumar</namePart>
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    <abstract>This paper describes our system submitted in the shared task at COLING 2018 TRAC-1: Aggression Identification. The objective of this task was to predict online aggression spread through online textual post or comment. The dataset was released in two languages, English and Hindi. We submitted a single system for Hindi and a single system for English. Both the systems are based on an ensemble architecture where the individual models are based on Convoluted Neural Network and Support Vector Machine. Evaluation shows promising results for both the languages. The total submission for English was 30 and Hindi was 15. Our system on English facebook and social media obtained F1 score of 0.5151 and 0.5099 respectively where Hindi facebook and social media obtained F1 score of 0.5599 and 0.3790 respectively.</abstract>
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%0 Conference Proceedings
%T An Ensemble Approach for Aggression Identification in English and Hindi Text
%A Roy, Arjun
%A Kapil, Prashant
%A Basak, Kingshuk
%A Ekbal, Asif
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%S Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F roy-etal-2018-ensemble
%X This paper describes our system submitted in the shared task at COLING 2018 TRAC-1: Aggression Identification. The objective of this task was to predict online aggression spread through online textual post or comment. The dataset was released in two languages, English and Hindi. We submitted a single system for Hindi and a single system for English. Both the systems are based on an ensemble architecture where the individual models are based on Convoluted Neural Network and Support Vector Machine. Evaluation shows promising results for both the languages. The total submission for English was 30 and Hindi was 15. Our system on English facebook and social media obtained F1 score of 0.5151 and 0.5099 respectively where Hindi facebook and social media obtained F1 score of 0.5599 and 0.3790 respectively.
%U https://aclanthology.org/W18-4408
%P 66-73

Markdown (Informal)

[An Ensemble Approach for Aggression Identification in English and Hindi Text](https://aclanthology.org/W18-4408) (Roy et al., TRAC 2018)

• An Ensemble Approach for Aggression Identification in English and Hindi Text (Roy et al., TRAC 2018)

ACL

• Arjun Roy, Prashant Kapil, Kingshuk Basak, and Asif Ekbal. 2018. An Ensemble Approach for Aggression Identification in English and Hindi Text. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 66–73, Santa Fe, New Mexico, USA. Association for Computational Linguistics.