@InProceedings{son-EtAl:2017:Short,
  author    = {Son, Youngseo  and  Buffone, Anneke  and  Raso, Joe  and  Larche, Allegra  and  Janocko, Anthony  and  Zembroski, Kevin  and  Schwartz, H. Andrew  and  Ungar, Lyle},
  title     = {Recognizing Counterfactual Thinking in Social Media Texts},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {654--658},
  abstract  = {Counterfactual statements, describing events that did not occur and their
	consequents, have been studied in areas including problem-solving, affect
	management, and behavior regulation. People with more counterfactual thinking
	tend to perceive life events as more personally meaningful. Nevertheless,
	counterfactuals have not been studied in computational linguistics. We create a
	counterfactual tweet dataset and explore approaches for detecting
	counterfactuals using rule-based and supervised statistical approaches. A
	combined rule-based and statistical approach yielded the best results (F1 =
	0.77) outperforming either approach used alone.},
  url       = {http://aclweb.org/anthology/P17-2103}
}