In this paper we present an approach to automatic authorship attribution dealing with real-world (or unrestricted) text. Our method is based on the computational analysis of the input text using a text-processing tool. Besides the style markers relevant to the output of this tool we also use analysis-dependent style markers, that is, measures that represent the way in which the text has been processed. No word frequency counts, nor other lexically-based measures are taken into account. We show that the proposed set of style markers is able to distinguish texts of various authors of a weekly newspaper using multiple.
