@InProceedings{ccoltekin-rama:2016:VarDial3,
  author    = {\c{C}\"{o}ltekin, \c{C}a\u{g}rı  and  Rama, Taraka},
  title     = {Discriminating Similar Languages with Linear SVMs and Neural Networks},
  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     = {15--24},
  abstract  = {This paper describes the systems we experimented with for participating in  
	the discriminating between similar languages (DSL) shared task 2016.  We    
	submitted results of a single system based on support vector machines (SVM) 
	with linear kernel and using character ngram features, which obtained the   
	first rank at the closed training track for test set A.  Besides the linear 
	SVM, we also report additional experiments with a number of deep learning   
	architectures. Despite our intuition that non-linear deep learning methods 
	should be advantageous, linear models seems to fare better in this task, at 
	least with the amount of data and the amount of effort we spent on tuning   
	these models.},
  url       = {http://aclweb.org/anthology/W16-4802}
}