Authorship attribution of text samples using neural networks and Bayesian classifiers
Bradley P. Kjell · 2002
Previous work has shown that statistics of letter pairs extracted from text samples can be effective in discriminating between two authors writing in a similar style. This paper extends that work by using n-tuples for n from 1 to 5. The features used in classification are the relative frequencies of the tuples, transformed with a KL transform. Both three layer neural network classifiers and Bayesian classifiers are used with these features to classify text samples from two similar authors. The most effective combination was 2-tuples used with a neural network classifier, although other combinations did nearly as well.>