@InProceedings{hitschler-vandenberg-rehbein:2017:StyVa,
  author    = {Hitschler, Julian  and  van den Berg, Esther  and  Rehbein, Ines},
  title     = {Authorship Attribution with Convolutional Neural Networks and POS-Eliding},
  booktitle = {Proceedings of the Workshop on Stylistic Variation},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {53--58},
  abstract  = {We use a convolutional neural network to perform authorship identification on a
	very homogeneous dataset of scientific publications. In order to investigate
	the effect of domain biases, we obscure words below a certain frequency
	threshold, retaining only their POS-tags. This procedure improves test
	performance due to better generalization on unseen data. Using our method, we
	are able to predict the authors of scientific publications in the same
	discipline at levels well above chance.},
  url       = {http://www.aclweb.org/anthology/W17-4907}
}