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Literary and Linguistic Computing Advance Access originally published online on October 22, 2008
Literary and Linguistic Computing 2008 23(4):425-442; doi:10.1093/llc/fqn022
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© The Author 2008. Published by Oxford University Press on behalf of ALLC and ACH. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

An algorithm for automated authorship attribution using neural networks

Matt Tearle, Kye Taylor and Howard Demuth

University of Colorado, Boulder, CO, USA

Correspondence: Matt Tearle, The MathWorks, 3 Apple Hill Dr, Natick, MA 01760, USA. E-mail: mtearle{at}gmail.com

  Abstract

We present an algorithm as evidence of the possibility of a truly automated stylometric authorship attribution tool, based on committees of artificial neural networks. Neural networks have an advantage over traditional statistical stylometry in that they are inherently nonlinear, and therefore can consider nonlinear interactions between stylometric variables. The algorithm presented (1) is intended to demonstrate the feasibility of an automated approach using neural networks and (2) highlights important areas for further research. We present results of two separate test experiments—Shakespeare and Marlowe, and the Federalist Papers—as a demonstration of the method's; generality. In both cases, our algorithm produces committees that correctly predict the test works, without requiring the usual precursory statistical study to determine efficacious stylometric measures.


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