Skip to main content
Article
Automatic Source Attribution of Text: A Neural Networks Approach
Proceedings of the 18th International Joint Conference on Neural Networks 2005: Montreal, Canada
  • Foaad Khosmood
  • Franz J. Kurfess, California Polytechnic State University - San Luis Obispo
Publication Date
8-1-2005
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
Publisher statement
Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Citation Information
Foaad Khosmood and Franz J. Kurfess. "Automatic Source Attribution of Text: A Neural Networks Approach" Proceedings of the 18th International Joint Conference on Neural Networks 2005: Montreal, Canada (2005) p. 2718 - 2723
Available at: http://works.bepress.com/foaad/1/