  Statistical methods have been widely employed in recent years to grasp many
language properties. The application of such techniques have allowed an
improvement of several linguistic applications, which encompasses machine
translation, automatic summarization and document classification. In the
latter, many approaches have emphasized the semantical content of texts, as it
is the case of bag-of-word language models. This approach has certainly yielded
reasonable performance. However, some potential features such as the structural
organization of texts have been used only on a few studies. In this context, we
probe how features derived from textual structure analysis can be effectively
employed in a classification task. More specifically, we performed a supervised
classification aiming at discriminating informative from imaginative documents.
Using a networked model that describes the local topological/dynamical
properties of function words, we achieved an accuracy rate of up to 95%, which
is much higher than similar networked approaches. A systematic analysis of
feature relevance revealed that symmetry and accessibility measurements are
among the most prominent network measurements. Our results suggest that these
measurements could be used in related language applications, as they play a
complementary role in characterizing texts.
