  With the increasing number of texts made available on the Internet, many
applications have relied on text mining tools to tackle a diversity of
problems. A relevant model to represent texts is the so-called word adjacency
(co-occurrence) representation, which is known to capture mainly syntactical
features of texts.In this study, we introduce a novel network representation
that considers the semantic similarity between paragraphs. Two main properties
of paragraph networks are considered: (i) their ability to incorporate
characteristics that can discriminate real from artificial, shuffled
manuscripts and (ii) their ability to capture syntactical and semantic textual
features. Our results revealed that real texts are organized into communities,
which turned out to be an important feature for discriminating them from
artificial texts. Interestingly, we have also found that, differently from
traditional co-occurrence networks, the adopted representation is able to
capture semantic features. Additionally, the proposed framework was employed to
analyze the Voynich manuscript, which was found to be compatible with texts
written in natural languages. Taken together, our findings suggest that the
proposed methodology can be combined with traditional network models to improve
text classification tasks.
