@InProceedings{hanani-qaroush-taylor:2016:VarDial3,
  author    = {Hanani, Abualsoud  and  Qaroush, Aziz  and  Taylor, Stephen},
  title     = {Classifying ASR Transcriptions According to Arabic Dialect},
  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     = {126--134},
  abstract  = {We describe several systems for identifying short samples of Arabic dialects.
	The systems were prepared for the shared task of the 2016 DSL
	Workshop.
	Our best system, an SVM using character tri-gram features,
	achieved an accuracy on the test data for the task of 0.4279, compared to
	a baseline of 0.20 for chance guesses or 0.2279 if we had always chosen the
	same most frequent class in the test set. This compares with the results
	of the team with the best weighted F1 score, which was
	an accuracy of 0.5117.
	The team entries seem to fall into cohorts, with 
	all the teams in a cohort within a
	standard-deviation of each other, and our three entries are in the third
	cohort, which is about seven standard deviations from the top.},
  url       = {http://aclweb.org/anthology/W16-4817}
}