@inproceedings{noecker-jr-ryan-2012-distractorless,
    title = "Distractorless Authorship Verification",
    author = "Noecker Jr, John  and
      Ryan, Michael",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Do{\u{g}}an, Mehmet U{\u{g}}ur  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
    month = may,
    year = "2012",
    address = "Istanbul, Turkey",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-emnlp/L12-1090/",
    pages = "785--789",
    abstract = "Authorship verification is the task of, given a document and a candi- date author, determining whether or not the document was written by the candi- date author. Traditional approaches to authorship verification have revolved around a “candidate author vs. everything else” approach. Thus, perhaps the most important aspect of performing authorship verification on a document is the development of an appropriate distractor set to represent “everything not the candidate author”. The validity of the results of such experiments hinges on the ability to develop an appropriately representative set of distractor documents. Here, we propose a method for performing authorship verification without the use of a distractor set. Using only training data from the candidate author, we are able to perform authorship verification with high confidence (greater than 90{\%} accuracy rates across a large corpus)."
}