Linguistic Networks are structures that allow us to model the characteristics of human language through a graph-like schema. This kind of modelization has proven to be useful while dealing with natural language processing tasks. In this paper, we first present and discuss the state of the art of recent semantic relatedness methods from a network-centric point of view. That is, we are interested in the types of networks used to solve practical semantic tasks. In order to address some of the shortcomings in the studied approaches, we propose a hybrid linguistic structure that takes into account lexical and syntactical language information. We show our model's practicality with a proof of concept: we set to solve word sense disambiguation and induction while using the presented network schema. Our modelization aims to shed light into ways of combining distinct types of linguistic information in order to take advantage of each of its components' unique characteristics.