Automatic source attribution of text: a neural networks approach

Recent advances in automatic authorship attribution have been promising. Relatively new techniques such as N-gram analysis have shown important improvements in accuracy by A.P. Engelbrecht (2002). Much of the work in this area does remain in the realm of statistics best suited for human assistance rather than autonomous attribution in "computer and humanities" by N. Fakotakis et al (2001). While there have been attempts at using neural networks in the area in the past, they have been extremely limited and problem-specific in "proceedings EACL" by N. Fakotakis et al (1999). 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.