@InProceedings{caselli-inel:2018:W18-43,
  author    = {Caselli, Tommaso  and  Inel, Oana},
  title     = {Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation},
  booktitle = {Proceedings of the Workshop Events and Stories in the News 2018},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, U.S.A},
  publisher = {Association for Computational Linguistics},
  pages     = {44--54},
  abstract  = {This paper describes a crowdsourcing experiment on the annotation of plot-like structures in English news articles. CrowdThruth methodology and metrics have been applied to select valid annotations from the crowd. We further run an in-depth analysis of the annotated data by comparing them with available expert data. Our results show a valuable use of crowdsourcing annotations for such complex semantic tasks, and suggest a new annotation approach which combine crowd and experts.},
  url       = {http://www.aclweb.org/anthology/W18-4306}
}