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        <title>Continuous N-gram Representations for Authorship Attribution</title>
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    <abstract>This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.</abstract>
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%0 Conference Proceedings
%T Continuous N-gram Representations for Authorship Attribution
%A Sari, Yunita
%A Vlachos, Andreas
%A Stevenson, Mark
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 apr
%I Association for Computational Linguistics
%C Valencia, Spain
%F sari-etal-2017-continuous
%X This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.
%U https://aclanthology.org/E17-2043
%P 267-273

Markdown (Informal)

[Continuous N-gram Representations for Authorship Attribution](https://aclanthology.org/E17-2043) (Sari et al., EACL 2017)

• Continuous N-gram Representations for Authorship Attribution (Sari et al., EACL 2017)

ACL

• Yunita Sari, Andreas Vlachos, and Mark Stevenson. 2017. Continuous N-gram Representations for Authorship Attribution. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 267–273, Valencia, Spain. Association for Computational Linguistics.