@InProceedings{chen-lee:2017:BEA,
  author    = {Chen, Lei  and  Lee, Chong Min},
  title     = {Predicting Audience's Laughter During Presentations Using Convolutional Neural Network},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {86--90},
  abstract  = {Public speakings play important roles in schools and work places and properly
	using humor contributes to effective presentations. For the purpose of
	automatically evaluating speakers' humor usage, we build a presentation corpus
	containing humorous utterances based on TED talks. Compared to previous data
	resources supporting humor recognition research, ours has several advantages,
	including (a) both positive and negative instances coming from a homogeneous
	data set, (b) containing a large number of speakers, and (c) being open.
	Focusing on using lexical cues for humor recognition, we systematically compare
	a newly emerging text classification method based on Convolutional Neural
	Networks (CNNs) with a well-established conventional method using linguistic
	knowledge. The advantages of the CNN method are both getting higher detection
	accuracies and being able to learn essential features automatically.},
  url       = {http://www.aclweb.org/anthology/W17-5009}
}