Continuous N-gram Representations for Authorship Attribution

Yunita Sari, Andreas Vlachos, Mark Stevenson

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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.
Anthology ID:
E17-2043
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
267–273
Language:
URL:
https://aclanthology.org/E17-2043
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Continuous N-gram Representations for Authorship Attribution (Sari et al., EACL 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/E17-2043.pdf
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