Abstract
Neural Networks have recently been a matter of extensive research and popularity. Their application has increased considerably in areas in which we are presented with a large amount of data and we have to identify an underlying pattern. This paper will look at their application to stylometry. We believe that statistical methods of attributing authorship can be coupled effectively with neural networks to produce a very powerful classification tool. We illustrate this with an example of a famous case of disputed authorship, The Federalist Papers. Our method assigns the disputed papers to Madison, a result which is consistent with previous work on the subject.
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Fiona J. Tweedie is a research student and tutor at the University ot the West of England, Bristol, currently working on the provenance of “De Doctrina Christiana”, attributed to John Milton. She has presented papers at the ACH/ALLC conference in 1995 and has forthcoming papers in Forensic Linguistics and Revue.
Sameer Singh is a research student and tutor at the University of the West of England, Bristol, working in the application of artificially intelligent methods and statistics for quantifying language disorders. His main research interests include neural networks, fuzzy logic, expert systems and linguistic computing.
David I. Holmes is a Principal Lecturer in Statistics at the University of the West, Bristol. He has published several papers on the statistical analysis of literary style in journals including the Journal of the Royal Statistical Society and History and Computing. He has presented papers at ACH/ALLC conferences in 1991, 1993 and 1995.
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Tweedie, F.J., Singh, S. & Holmes, D.I. Neural network applications in stylometry: The Federalist Papers . Comput Hum 30, 1–10 (1996). https://doi.org/10.1007/BF00054024
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DOI: https://doi.org/10.1007/BF00054024