@InProceedings{chan-EtAl:2017:BEA,
  author    = {Chan, Sophia  and  Honari Jahromi, Maryam  and  Benetti, Benjamin  and  Lakhani, Aazim  and  Fyshe, Alona},
  title     = {Ensemble Methods 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     = {217--223},
  abstract  = {Our team—Uvic-NLP—explored and evaluated a variety of lexical features for
	Native Language Identification (NLI) within the framework of ensemble methods.
	Using a subset of the highest performing features, we train Support Vector
	Machines (SVM) and Fully Connected Neural Networks (FCNN) as base classifiers,
	and test different methods for combining their outputs. Restricting our scope
	to the closed essay track in the NLI Shared Task 2017, we find that our best
	SVM ensemble achieves an F1 score of 0.8730 on the test set.},
  url       = {http://www.aclweb.org/anthology/W17-5023}
}