@InProceedings{cimino-dellorletta:2017:BEA,
  author    = {Cimino, Andrea  and  Dell'Orletta, Felice},
  title     = {Stacked Sentence-Document Classifier Approach for Improving 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     = {430--437},
  abstract  = {In this paper, we describe the approach of the ItaliaNLP Lab team to native
	language identification and discuss the results we submitted as participants to
	the essay track of NLI Shared Task 2017. We introduce for the first time a
	2-stacked sentence-document architecture for native language identification
	that is able to exploit both local sentence information and a wide set of
	general-purpose features qualifying the lexical and grammatical structure of
	the whole document. When evaluated on the official test set, our
	sentence-document stacked architecture obtained the best result among all the
	participants of the essay track with an F1 score of 0.8818.},
  url       = {http://www.aclweb.org/anthology/W17-5049}
}