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        <title>Assessing Convincingness of Arguments in Online Debates with Limited Number of Features</title>
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    <abstract>We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016). Rather than using argument specific feature values, we measure feature values relative to the average value in the debate, allowing us to determine argument convincingness with fewer features (between 5 and 35) than normally used for natural language processing tasks. We use a simple forward-feeding neural network for this task and achieve an accuracy of 0.77 which is comparable to the accuracy obtained using 64k features and a support vector machine by Habernal and Gurevych.</abstract>
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%0 Conference Proceedings
%T Assessing Convincingness of Arguments in Online Debates with Limited Number of Features
%A Chalaguine, Lisa Andreevna
%A Schulz, Claudia
%Y Kunneman, Florian
%Y Iñurrieta, Uxoa
%Y Camilleri, John J.
%Y Ardanuy, Mariona Coll
%S Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F chalaguine-schulz-2017-assessing
%X We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016). Rather than using argument specific feature values, we measure feature values relative to the average value in the debate, allowing us to determine argument convincingness with fewer features (between 5 and 35) than normally used for natural language processing tasks. We use a simple forward-feeding neural network for this task and achieve an accuracy of 0.77 which is comparable to the accuracy obtained using 64k features and a support vector machine by Habernal and Gurevych.
%U https://aclanthology.org/E17-4008
%P 75-83

Markdown (Informal)

[Assessing Convincingness of Arguments in Online Debates with Limited Number of Features](https://aclanthology.org/E17-4008) (Chalaguine & Schulz, EACL 2017)

• Assessing Convincingness of Arguments in Online Debates with Limited Number of Features (Chalaguine & Schulz, EACL 2017)

ACL

• Lisa Andreevna Chalaguine and Claudia Schulz. 2017. Assessing Convincingness of Arguments in Online Debates with Limited Number of Features. In Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics, pages 75–83, Valencia, Spain. Association for Computational Linguistics.