@InProceedings{cattle-ma:2017:SemEval,
  author    = {Cattle, Andrew  and  Ma, Xiaojuan},
  title     = {SRHR at SemEval-2017 Task 6: Word Associations for Humour 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     = {401--406},
  abstract  = {This paper explores the role of semantic relatedness features, such as word
	associations, in humour recognition. Specifically, we examine the task of
	inferring pairwise humour judgments in Twitter hashtag wars. We examine a
	variety of word association features derived from University of Southern
	Florida Free Association Norms (USF) and the Edinburgh Associative Thesaurus
	(EAT) and find that word association-based features outperform Word2Vec
	similarity, a popular semantic relatedness measure. Our system achieves an
	accuracy of 56.42% using a combination of unigram perplexity, bigram
	perplexity, EAT difference (tweet-avg), USF forward (max), EAT difference
	(word-avg), USF difference (word-avg), EAT forward (min), USF difference
	(tweet-max), and EAT backward (min).},
  url       = {http://www.aclweb.org/anthology/S17-2067}
}