  Writing style is a combination of consistent decisions associated with a
specific author at different levels of language production, including lexical,
syntactic, and structural. In this paper, we introduce a style-aware neural
model to encode document information from three stylistic levels and evaluate
it in the domain of authorship attribution. First, we propose a simple way to
jointly encode syntactic and lexical representations of sentences.
Subsequently, we employ an attention-based hierarchical neural network to
encode the syntactic and semantic structure of sentences in documents while
rewarding the sentences which contribute more to capturing the writing style.
Our experimental results, based on four benchmark datasets, reveal the benefits
of encoding document information from all three stylistic levels when compared
to the baseline methods in the literature.
