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Literary and Linguistic Computing 2000 15(2):187-198; doi:10.1093/llc/15.2.187
© 2000 by Association for Literary & Linguistic Computing
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Computational stylistics using artificial neural networks

S WaughZ, A AdamsY and F TweedieZZ

University of Tasmania, Hobart, Australia ZZ University of Glasgow, Glasgow, UK Y Present address: Department of Mathematics and Computing, The University of the South Pacific, Suva, Fiji Z Corresponding author address: c/o Air Operations Division, Defence Science and Technology Organisation, PO Box 4331, Melbourne, Victoria 3001, Australia E-mail: sam.waugh@dsto.defence.gov.au

Previous work in using artificial neural networks for computational stylistics has concentrated on using large, arbitrary network structures. This paper examines the use of the Cascade-Correlation algorithm for the construction of minimal networks. We find that a number of problems in computational stylistics with a large number of variables but a limited number of training examples may be solved successfully without resorting to large networks. The issue of redundancy in the data is also considered.


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