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.

I. Introduction

We define automatic source attribution as the ability for an autonomous process to determine the source of a previously unexamined piece of text. A software system designed to follow such a process would analyze a set of input corpora, and construct a neural network to engage in attribution. It would then train the network with the corpora; apply the sample texts and determine attribution. For our source recognition problem, our system constructs a 5 layer, 420 Million-connection neural network. It is able to correctly attribute sample texts, previously unexamined by the system. Specifically, we conduct three sets of experiments to test the ability of the system: broad categorization, narrow categorization and minimal-sample categorization.