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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CITATION STYLE
Shrestha, P., Sierra, S., González, F. A., Rosso, P., Montes-Y-Gómez, M., & Solorio, T. (2017). Convolutional neural networks for authorship attribution of short texts. In 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference (Vol. 2, pp. 669–674). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-2106
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