  The problem of unveiling the author of a given text document from multiple
candidate authors is called authorship attribution. Manifold word-based
stylistic markers have been successfully used in deep learning methods to deal
with the intrinsic problem of authorship attribution. Unfortunately, the
performance of word-based authorship attribution systems is limited by the
vocabulary of the training corpus. Literature has recommended character-based
stylistic markers as an alternative to overcome the hidden word problem.
However, character-based methods often fail to capture the sequential
relationship of words in texts which is a chasm for further improvement. The
question addressed in this paper is whether it is possible to address the
ambiguity of hidden words in text documents while preserving the sequential
context of words. Consequently, a method based on bidirectional long short-term
memory (BLSTM) with a 2-dimensional convolutional neural network (CNN) is
proposed to capture sequential writing styles for authorship attribution. The
BLSTM was used to obtain the sequential relationship among characteristics
using subword information. The 2-dimensional CNN was applied to understand the
local syntactical position of the style from unlabeled input text. The proposed
method was experimentally evaluated against numerous state-of-the-art methods
across the public corporal of CCAT50, IMDb62, Blog50, and Twitter50.
Experimental results indicate accuracy improvement of 1.07\%, and 0.96\% on
CCAT50 and Twitter, respectively, and produce comparable results on the
remaining datasets.
