  We investigate the nature of written human language within the framework of
complex network theory. In particular, we analyse the topology of Orwell's
\textit{1984} focusing on the local properties of the network, such as the
properties of the nearest neighbors and the clustering coefficient. We find a
composite power law behavior for both the average nearest neighbor's degree and
average clustering coefficient as a function of the vertex degree. This implies
the existence of different functional classes of vertices. Furthermore we find
that the second order vertex correlations are an essential component of the
network architecture. To model our empirical results we extend a previously
introduced model for language due to Dorogovtsev and Mendes. We propose an
accelerated growing network model that contains three growth mechanisms: linear
preferential attachment, local preferential attachment and the random growth of
a pre-determined small finite subset of initial vertices. We find that with
these elementary stochastic rules we are able to produce a network showing
syntactic-like structures.
