@InProceedings{malmasi-zampieri:2016:VarDial3,
  author    = {Malmasi, Shervin  and  Zampieri, Marcos},
  title     = {Arabic Dialect Identification in Speech Transcripts},
  booktitle = {Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {106--113},
  abstract  = {In this paper we describe a system developed to identify a set of four regional
	Arabic dialects (Egyptian, Gulf, Levantine, North African) and Modern Standard
	Arabic (MSA) in a transcribed speech corpus. We competed under the team name
	MAZA in the Arabic Dialect Identification sub-task of the 2016 Discriminating
	between Similar Languages (DSL) shared task. Our system
	achieved an F1-score of 0.51 in the closed training track, ranking first among
	the 18 teams that participated in the sub-task. Our system utilizes a
	classifier ensemble with a set of linear models as base classifiers. We
	experimented with three different ensemble fusion strategies, with the
	mean probability approach providing the best performance.},
  url       = {http://aclweb.org/anthology/W16-4814}
}