Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Style-aware Neural Model with Application in Authorship Attribution   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.

21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th