| Abstract: | Authorship identification can be viewed as a text categorization task.
However, in this task the most frequent features appear to be the most important
discriminators, there is usually a shortage of training texts, and the training texts
are rarely evenly distributed over the authors. To cope with these problems, we
propose tensors of second order for representing the stylistic properties of texts.
Our approach requires the calculation of much fewer parameters in comparison
to the traditional vector space representation. We examine various methods for
building appropriate tensors taking into account that similar features should be
placed in the same neighborhood. Based on an existing generalization of SVM
able to handle tensors we perform experiments on corpora controlled for genre
and topic and show that the proposed approach can effectively handle cases
where only limited training texts are available. |
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Greek