@InProceedings{kulmizev-EtAl:2017:BEA,
  author    = {Kulmizev, Artur  and  Blankers, Bo  and  Bjerva, Johannes  and  Nissim, Malvina  and  van Noord, Gertjan  and  Plank, Barbara  and  Wieling, Martijn},
  title     = {The Power of Character N-grams in Native Language Identification},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {382--389},
  abstract  = {In this paper, we explore the performance of a linear SVM trained on language 
	independent character features for the NLI Shared Task 2017. Our basic system
	(GRONINGEN) achieves the best performance (87.56 F1-score) on the evaluation
	set using only 1-9 character n-grams as features. We compare this against
	several ensemble and meta-classifiers in order to examine how the linear system
	fares when combined with other, especially non-linear classifiers. Special
	emphasis is placed on the topic bias that exists by virtue of the assessment
	essay prompt distribution.},
  url       = {http://www.aclweb.org/anthology/W17-5043}
}