Title

Automatic Source Attribution of Text: A Neural Networks Approach

Author Info

Foaad Khosmood
Franz J. Kurfess, California Polytechnic State University - San Luis ObispoFollow

Recommended Citation

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.

Abstract

Recent advances in automatic authorship attribution have been promising. Relatively new techniques such as N-gram analysis have shown important improvements in accuracy [2]. Much of the work in this area does remain in the realm of statistics best suited for human assistance rather than autonomous attribution [6]. While there have been attempts at using neural networks in the area in the past, they have been extremely limited and problem-specific [7]. 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.

Disciplines

Computer Sciences

Copyright

2005 IEEE.

Publisher statement

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