@InProceedings{markov-EtAl:2017:BEA,
  author    = {Markov, Ilia  and  Chen, Lingzhen  and  Strapparava, Carlo  and  Sidorov, Grigori},
  title     = {CIC-FBK Approach to 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     = {374--381},
  abstract  = {We present the CIC-FBK system, which took part in the Native Language
	Identification (NLI) Shared Task 2017. Our approach combines features commonly
	used in previous NLI research, i.e., word n-grams, lemma n-grams,
	part-of-speech n-grams, and function words, with recently introduced character
	n-grams from misspelled words, and features that are novel in this task, such
	as typed character n-grams, and syntactic n-grams of words and of syntactic
	relation tags. We use log-entropy weighting scheme and perform classification
	using the Support Vector Machines (SVM) algorithm. Our system achieved 0.8808
	macro-averaged F1-score and shared the 1st rank in the NLI Shared Task 2017
	scoring.},
  url       = {http://www.aclweb.org/anthology/W17-5042}
}