Summary of the paper
| Title | Distractorless Authorship Verification |
|---|---|
| Authors | John Noecker Jr and Michael Ryan |
| 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). |
| Topics | Person Identification, Statistical and machine learning methods, Information Extraction, Information Retrieval |
Full paper  |
Distractorless Authorship Verification |
| Bibtex | @InProceedings{NOECKERJR12.238, author = {John Noecker Jr and Michael Ryan}, title = {Distractorless Authorship Verification}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |