Despite its outward simplicity, co-occurrence networks have proven useful in many applications, such as in authorship recognition <cite>[9]</cite> , extractive summarization [10, 11, 12] , stylistic identification [13] and part-of-speech tagging [14] . Unfortunately, a major problem arising from the analyses performed with co-occurrence networks is the difficulty to provide a rigorous interpretation of the factors accounting for the success of the model. Therefore, future investigations should pursue a better interpretation at the network level aiming at the understanding of the fundamental properties of the language. Most importantly, it is clear from some recent studies [8, <cite>9</cite> ] that novel topological measurements should be introduced to capture a wider range of linguistic features.