@inproceedings{caselli-vossen-2017-event,
    title = "The Event {S}tory{L}ine Corpus: A New Benchmark for Causal and Temporal Relation Extraction",
    author = "Caselli, Tommaso  and
      Vossen, Piek",
    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",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/W17-2711/",
    doi = "10.18653/v1/W17-2711",
    pages = "77--86",
    abstract = "This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems."
}