Continuous N-gram Representations for Authorship Attribution

Sari, Y., Vlachos, A. and Stevenson, R.M. orcid.org/0000-0002-9483-6006 (2017) Continuous N-gram Representations for Authorship Attribution. In: Lapata, M., Blunsom, P. and Koller, A., (eds.) Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. European Chapter of the Association for Computational Linguistics (EACL 2017), 03-07 Apr 2017, Valencia, Spain. ACL . ISBN 978-1-945626-35-7

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.

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Authors/Creators:	Sari, Y. Vlachos, A. Stevenson, R.M. https://orcid.org/0000-0002-9483-6006
Copyright, Publisher and Additional Information:	© 2017 ACL. Article licensed on a Creative Commons Attribution 4.0 License
Dates:	Accepted: 1 February 2017 Published (online): 1 April 2017 Published: 1 April 2017
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	23 Feb 2017 16:12
Last Modified:	19 Dec 2022 13:35
Published Version:	https://www.aclweb.org/anthology/papers/E/E17/E17-...
Status:	Published
Publisher:	ACL
Refereed:	Yes

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Continuous N-gram Representations for Authorship Attribution

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