@InProceedings{donahue-romanov-rumshisky:2017:SemEval,
  author    = {Donahue, David  and  Romanov, Alexey  and  Rumshisky, Anna},
  title     = {HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {98--102},
  abstract  = {This paper describes the winning system for SemEval-2017 Task 6: \#HashtagWars:
	Learning a Sense of Humor. Humor detection has up until now been predominantly
	addressed using feature-based approaches. Our system utilizes recurrent deep
	learning methods with dense embeddings to predict humorous tweets from the
	@midnight show \#HashtagWars. In order to include both meaning and sound in the
	analysis, GloVe embeddings are combined with a novel phonetic representation to
	serve as input to an LSTM component. The output is combined with a
	character-based CNN model, and an XGBoost component in an ensemble model which
	achieves 0.675 accuracy on the evaluation data.},
  url       = {http://www.aclweb.org/anthology/S17-2010}
}