@inproceedings{ferracane-etal-2017-leveraging,
    title = "Leveraging Discourse Information Effectively for Authorship Attribution",
    author = "Ferracane, Elisa  and
      Wang, Su  and
      Mooney, Raymond",
    editor = "Kondrak, Greg  and
      Watanabe, Taro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/ingest_wac_2008/I17-1059/",
    pages = "584--593",
    abstract = "We explore techniques to maximize the effectiveness of discourse information in the task of authorship attribution. We present a novel method to embed discourse features in a Convolutional Neural Network text classifier, which achieves a state-of-the-art result by a significant margin. We empirically investigate several featurization methods to understand the conditions under which discourse features contribute non-trivial performance gains, and analyze discourse embeddings."
}