@InProceedings{oh-EtAl:2017:BEA,
  author    = {Oh, Yoo Rhee  and  Jeon, Hyung-Bae  and  Song, Hwa Jeon  and  Lee, Yun-Kyung  and  Park, Jeon-Gue  and  Lee, Yun-Keun},
  title     = {A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017},
  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     = {413--422},
  abstract  = {This paper proposes a deep-learning based native-language identification (NLI)
	using a latent semantic analysis (LSA) as a participant (ETRI-SLP) of the NLI
	Shared Task 2017 where the NLI Shared Task 2017 aims to detect the native
	language of an essay or speech response of a standardized assessment of English
	proficiency for academic purposes. To this end, we use the six unit forms of a
	text data such as character 4/5/6-grams and word 1/2/3-grams. For each unit
	form of text data, we convert it into a count-based vector, extract a 2000-rank
	LSA feature, and perform a linear discriminant analysis (LDA) based dimension
	reduction. From the count-based vector or the LSA-LDA feature, we also obtain
	the output prediction values of a support vector machine (SVM) based
	classifier, the output prediction values of a deep neural network (DNN) based
	classifier, and the bottleneck values of a DNN based classifier. In order to
	incorporate the various kinds of text-based features and a speech-based
	i-vector feature, we design two DNN based ensemble classifiers for late fusion
	and early fusion, respectively. From the NLI experiments, the F1 (macro) scores
	are obtained as 0.8601, 0.8664, and 0.9220 for the essay track, the speech
	track, and the fusion track, respectively. The proposed method has comparable
	performance to the top-ranked teams for the speech and fusion tracks, although
	it has slightly lower performance for the essay track.},
  url       = {http://www.aclweb.org/anthology/W17-5047}
}