Published in Proceedings of the 18th International Joint Conference on Neural Networks 2005: Montreal, Canada, August 1, 2005, pages 2718-2723.
The definitive version is available at https://doi.org/10.1109/IJCNN.2005.1556355.
Recent advances in automatic authorship attribution have been promising. Relatively new techniques such as N-gram analysis have shown important improvements in accuracy . Much of the work in this area does remain in the realm of statistics best suited for human assistance rather than autonomous attribution . While there have been attempts at using neural networks in the area in the past, they have been extremely limited and problem-specific . This paper addresses the latter points by demonstrating a practical and truly autonomous attribution process using neural networks. Furthermore, we use a word-frequency classification technique to demonstrate the feasibility of this process in particular and the applications of neural networks to textual analysis in general.
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