@InProceedings{li-zou:2017:BEA,
  author    = {Li, Wen  and  Zou, Liang},
  title     = {Classifier Stacking for 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     = {390--397},
  abstract  = {This paper reports our contribution (team WLZ) to the NLI Shared Task 2017
	(essay track). We first extract lexical and syntactic features from the essays,
	perform feature weighting and selection, and train linear support vector
	machine (SVM) classifiers each on an individual feature type. The output of
	base classifiers, as probabilities for each class, are then fed into a
	multilayer perceptron to predict the native language of the author. We also
	report the performance of each feature type, as well as the best features of a
	type. Our system achieves an accuracy of 86.55%, which is among the best
	performing systems of this shared task.},
  url       = {http://www.aclweb.org/anthology/W17-5044}
}