@InProceedings{cattle-ma:2016:PEOPLES,
  author    = {Cattle, Andrew  and  Ma, Xiaojuan},
  title     = {Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games},
  booktitle = {Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {70--79},
  abstract  = {This paper explores humour recognition for Twitter-based hashtag games. Given
	their popularity, frequency, and relatively formulaic nature, these games make
	a good target for computational humour research and can leverage Twitter likes
	and retweets as humour judgments. In this work, we use pair-wise relative
	humour judgments to examine several measures of semantic relatedness between
	setups and punchlines on a hashtag game corpus we collected and annotated.
	Results show that perplexity, Normalized Google Distance, and free-word
	association-based features are all useful in identifying "funnier" hashtag game
	responses. In fact, we provide empirical evidence that funnier punchlines tend
	to be more obscure, although more obscure punchlines are not necessarily rated
	funnier. Furthermore, the asymmetric nature of free-word association features
	allows us to see that while punchlines should be harder to predict given a
	setup, they should also be relatively easy to understand in context.},
  url       = {http://aclweb.org/anthology/W16-4308}
}