  The automatic disambiguation of word senses (i.e., the identification of
which of the meanings is used in a given context for a word that has multiple
meanings) is essential for such applications as machine translation and
information retrieval, and represents a key step for developing the so-called
Semantic Web. Humans disambiguate words in a straightforward fashion, but this
does not apply to computers. In this paper we address the problem of Word Sense
Disambiguation (WSD) by treating texts as complex networks, and show that word
senses can be distinguished upon characterizing the local structure around
ambiguous words. Our goal was not to obtain the best possible disambiguation
system, but we nevertheless found that in half of the cases our approach
outperforms traditional shallow methods. We show that the hierarchical
connectivity and clustering of words are usually the most relevant features for
WSD. The results reported here shine light on the relationship between semantic
and structural parameters of complex networks. They also indicate that when
combined with traditional techniques the complex network approach may be useful
to enhance the discrimination of senses in large texts
