@inproceedings{hitschler-etal-2017-authorship,
    title = "Authorship Attribution with Convolutional Neural Networks and {POS}-Eliding",
    author = "Hitschler, Julian  and
      van den Berg, Esther  and
      Rehbein, Ines",
    editor = "Brooke, Julian  and
      Solorio, Thamar  and
      Koppel, Moshe",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-4907/",
    doi = "10.18653/v1/W17-4907",
    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."
}