The mesoscopic structure of complex networks has proven a powerful level of description
to understand the linchpins of the system represented by the network. Nevertheless, the
mapping of a series of relationships between elements, in terms of a graph, is sometimes
not straightforward. Given that all the information we would extract using complex network
tools depend on this initial graph, it is mandatory to preprocess the data to build it on
in the most accurate manner. Here we propose a procedure to build a network, attending
only to statistically significant relations between constituents. We use a paradigmatic
example of word associations to show the development of our approach. Analyzing the modular
structure of the obtained network we are able to disentangle categorical relations,
disambiguating words with success that is comparable to the best algorithms designed to
the same end.