@InProceedings{mahajan-zaveri:2017:SemEval,
  author    = {Mahajan, Rutal  and  Zaveri, Mukesh},
  title     = {SVNIT $@$ SemEval 2017 Task-6: Learning a Sense of Humor Using Supervised Approach},
  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     = {411--415},
  abstract  = {This paper describes the system devel-oped for SemEval 2017 task 6:
	\#HashTagWars -Learning a Sense of Hu-mor. Learning to recognize sense of hu-mor
	is the important task for language understanding applications. Different set of
	features based on frequency of words, structure of tweets and semantics are
	used in this system to identify the presence of humor in tweets. Supervised
	machine learning approaches, Multilayer percep-tron and Na\"{i}ve Bayes are used
	to classify the tweets in to three level of sense of humor. For given Hashtag,
	the system finds the funniest tweet and predicts the amount of funniness of all
	the other tweets. In official submitted runs, we have achieved 0.506 accuracy
	using mul-tilayer perceptron in subtask-A and 0.938 distance in subtask-B.
	Using Na\"{i}ve bayes in subtask-B, the system achieved 0.949 distance. Apart from
	official runs, this system have scored 0.751 accuracy in subtask-A using SVM.
	But still there is a wide room for improvement in system.},
  url       = {http://www.aclweb.org/anthology/S17-2069}
}