@inproceedings{caselli-inel-2018-crowdsourcing,
    title = "Crowdsourcing {S}tory{L}ines: Harnessing the Crowd for Causal Relation Annotation",
    author = "Caselli, Tommaso  and
      Inel, Oana",
    editor = "Caselli, Tommaso  and
      Miller, Ben  and
      van Erp, Marieke  and
      Vossen, Piek  and
      Palmer, Martha  and
      Hovy, Eduard  and
      Mitamura, Teruko  and
      Caswell, David  and
      Brown, Susan W.  and
      Bonial, Claire",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W18-4306/",
    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."
}