@InProceedings{zhang-zhang-fu:2016:COLING,
  author    = {Zhang, Meishan  and  Zhang, Yue  and  Fu, Guohong},
  title     = {Tweet Sarcasm Detection Using Deep Neural Network},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2449--2460},
  abstract  = {Sarcasm detection has been modeled as a binary document classification task,
	with rich features being defined manually over input documents.
	Traditional models employ discrete manual features to address the task,
	with much research effect being devoted to the design of effective feature
	templates.
	We investigate the use of neural network for tweet sarcasm detection,
	and compare the effects of the continuous automatic features with discrete
	manual features.
	In particular, we use a bi-directional gated recurrent neural network to
	capture syntactic and semantic information over tweets locally,
	and a pooling neural network to extract contextual features automatically from
	history tweets.
	Results show that neural features give improved accuracies for sarcasm
	detection,
	with different error distributions compared with discrete manual features.},
  url       = {http://aclweb.org/anthology/C16-1231}
}