  Complex networks have been employed to model many real systems and as a
modeling tool in a myriad of applications. In this paper, we use the framework
of complex networks to the problem of supervised classification in the word
disambiguation task, which consists in deriving a function from the supervised
(or labeled) training data of ambiguous words. Traditional supervised data
classification takes into account only topological or physical features of the
input data. On the other hand, the human (animal) brain performs both low- and
high-level orders of learning and it has facility to identify patterns
according to the semantic meaning of the input data. In this paper, we apply a
hybrid technique which encompasses both types of learning in the field of word
sense disambiguation and show that the high-level order of learning can really
improve the accuracy rate of the model. This evidence serves to demonstrate
that the internal structures formed by the words do present patterns that,
generally, cannot be correctly unveiled by only traditional techniques.
Finally, we exhibit the behavior of the model for different weights of the low-
and high-level classifiers by plotting decision boundaries. This study helps
one to better understand the effectiveness of the model.
