  Writing style is a combination of consistent decisions at different levels of
language production including lexical, syntactic, and structural associated to
a specific author (or author groups). While lexical-based models have been
widely explored in style-based text classification, relying on content makes
the model less scalable when dealing with heterogeneous data comprised of
various topics. On the other hand, syntactic models which are
content-independent, are more robust against topic variance. In this paper, we
introduce a syntactic recurrent neural network to encode the syntactic patterns
of a document in a hierarchical structure. The model first learns the syntactic
representation of sentences from the sequence of part-of-speech tags. For this
purpose, we exploit both convolutional filters and long short-term memories to
investigate the short-term and long-term dependencies of part-of-speech tags in
the sentences. Subsequently, the syntactic representations of sentences are
aggregated into document representation using recurrent neural networks. Our
experimental results on PAN 2012 dataset for authorship attribution task shows
that syntactic recurrent neural network outperforms the lexical model with the
identical architecture by approximately 14% in terms of accuracy.
