@InProceedings{stamatatos:2017:EACLlong,
  author    = {Stamatatos, Efstathios},
  title     = {Authorship Attribution Using Text Distortion},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {1138--1149},
  abstract  = {Authorship attribution is associated with important applications in forensics
	and humanities research. A crucial point in this field is to quantify the
	personal style of writing, ideally in a way that is not affected by changes in
	topic or genre. In this paper, we present a novel method that enhances
	authorship attribution effectiveness by introducing a text distortion step
	before extracting stylometric measures. The proposed method attempts to mask
	topic-specific information that is not related to the personal style of
	authors. Based on experiments on two main tasks in authorship attribution,
	closed-set attribution and authorship verification, we demonstrate that the
	proposed approach can enhance existing methods especially under cross-topic
	conditions, where the training and test corpora do not match in topic.},
  url       = {http://www.aclweb.org/anthology/E17-1107}
}