@inproceedings{noecker-jr-ryan-2012-distractorless,
title = "Distractorless Authorship Verification",
author = "Noecker Jr, John and
Ryan, Michael",
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 = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/238_Paper.pdf",
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).",
}
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<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).</abstract>
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%0 Conference Proceedings
%T Distractorless Authorship Verification
%A Noecker Jr, John
%A Ryan, Michael
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 may
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F noecker-jr-ryan-2012-distractorless
%X 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).
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/238_Paper.pdf
%P 785-789
Markdown (Informal)
[Distractorless Authorship Verification](http://www.lrec-conf.org/proceedings/lrec2012/pdf/238_Paper.pdf) (Noecker Jr & Ryan, LREC 2012)
ACL
- John Noecker Jr and Michael Ryan. 2012. Distractorless Authorship Verification. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 785–789, Istanbul, Turkey. European Language Resources Association (ELRA).