Resumen:
Author pro ling consists in determining some demographic attributes -such as gender,
age, nationality, language, religion, and others- of an author for a given document. This
task, which has applications in elds such as forensics, security, or marketing, has been
approached from di erent areas, especially from linguistics and natural language process-
ing, by extracting di erent types of features from training documents, usually content-
and style-based features.
In this paper we address the problem by using several compression-inspired strategies
that generate diferent models without analyzing or extracting speci c features from the
textual content, making them style-oblivious approaches. We analyze the behavior of
these techniques, combine them and compare them with other state-of-the-art methods.
We show that they can be competitive in terms of accuracy, giving the best predictions
for some domains, and they are eficient in time performance.
