Cross-Domain Authorship Attribution Using Pre-trained Language Models

Author:

Barlas Georgios,Stamatatos Efstathios

Publisher

Springer International Publishing

Reference24 articles.

1. Bagnall, D.: Author identification using multi-headed recurrent neural networks. In: Working Notes of CLEF 2015 - Conference and Labs of the Evaluation forum (2015)

2. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019)

3. Ding, S., Fung, B., Iqbal, F., Cheung, W.: Learning stylometric representations for authorship analysis. IEEE Trans. Cybern. 49(1), 107–121 (2019)

4. Fourkioti, O., Symeonidis, S., Arampatzis, A.: Language models and fusion for authorship attribution. Inf. Process. Manag. 56(6), 102061 (2019)

5. Ge, Z., Sun, Y., Smith, M.J.T.: Authorship attribution using a neural network language model. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 4212–4213. AAAI Press (2016)

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