This paper presents work on using continuous
representations for authorship attribution.
In contrast to previous work,
which uses discrete feature representations,
our model learns continuous representations
for n-gram features via a neural
network jointly with the classification
layer. Experimental results demonstrate
that the proposed model outperforms the
state-of-the-art on two datasets, while producing
comparable results on the remaining
two
