@inproceedings{shrestha-etal-2017-convolutional,
title = "Convolutional Neural Networks for Authorship Attribution of Short Texts",
author = "Shrestha, Prasha and
Sierra, Sebastian and
Gonz{\'a}lez, Fabio and
Montes, Manuel and
Rosso, Paolo and
Solorio, Thamar",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2106",
pages = "669--674",
abstract = "We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams. We also present a strategy that improves model interpretability by estimating the importance of input text fragments in the predicted classification. The experimental evaluation shows that text CNNs perform competitively and are able to outperform previous methods.",
}
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<abstract>We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams. We also present a strategy that improves model interpretability by estimating the importance of input text fragments in the predicted classification. The experimental evaluation shows that text CNNs perform competitively and are able to outperform previous methods.</abstract>
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%0 Conference Proceedings
%T Convolutional Neural Networks for Authorship Attribution of Short Texts
%A Shrestha, Prasha
%A Sierra, Sebastian
%A González, Fabio
%A Montes, Manuel
%A Rosso, Paolo
%A Solorio, Thamar
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F shrestha-etal-2017-convolutional
%X We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams. We also present a strategy that improves model interpretability by estimating the importance of input text fragments in the predicted classification. The experimental evaluation shows that text CNNs perform competitively and are able to outperform previous methods.
%U https://aclanthology.org/E17-2106
%P 669-674
Markdown (Informal)
[Convolutional Neural Networks for Authorship Attribution of Short Texts](https://aclanthology.org/E17-2106) (Shrestha et al., EACL 2017)
ACL
- Prasha Shrestha, Sebastian Sierra, Fabio González, Manuel Montes, Paolo Rosso, and Thamar Solorio. 2017. Convolutional Neural Networks for Authorship Attribution of Short Texts. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 669–674, Valencia, Spain. Association for Computational Linguistics.