@InProceedings{kukovavcec-EtAl:2017:SemEval,
  author    = {Kukova\v{c}ec, Marin  and  Malenica, Juraj  and  Mr\v{s}i\'{c}, Ivan  and  \v{S}ajatovi\'{c}, Antonio  and  Alagi\'{c}, Domagoj  and  \v{S}najder, Jan},
  title     = {TakeLab at SemEval-2017 Task 6: \#RankingHumorIn4Pages},
  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     = {396--400},
  abstract  = {This paper describes our system for humor ranking in tweets within the SemEval
	2017 Task 6: \\#HashtagWars (6A and 6B). For both subtasks, we use an
	off-the-shelf gradient boosting model built on a rich set of features,
	handcrafted to provide the model with the external knowledge needed to better
	predict the humor in the text. The features capture various cultural references
	and specific humor patterns. Our system ranked 2nd (officially 7th) among 10
	submissions on the Subtask A and 2nd among 9 submissions on the Subtask B.},
  url       = {http://www.aclweb.org/anthology/S17-2066}
}