@inproceedings{wanzare-etal-2019-detecting,
    title = "Detecting Everyday Scenarios in Narrative Texts",
    author = "Wanzare, Lilian Diana Awuor  and
      Roth, Michael  and
      Pinkal, Manfred",
    editor = "Ferraro, Francis  and
      Huang, Ting-Hao {\textquoteleft}Kenneth{'}  and
      Lukin, Stephanie M.  and
      Mitchell, Margaret",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/W19-3410/",
    doi = "10.18653/v1/W19-3410",
    pages = "90--106",
    abstract = "Script knowledge consists of detailed information on everyday activities. Such information is often taken for granted in text and needs to be inferred by readers. Therefore, script knowledge is a central component to language comprehension. Previous work on representing scripts is mostly based on extensive manual work or limited to scenarios that can be found with sufficient redundancy in large corpora. We introduce the task of scenario detection, in which we identify references to scripts. In this task, we address a wide range of different scripts (200 scenarios) and we attempt to identify all references to them in a collection of narrative texts. We present a first benchmark data set and a baseline model that tackles scenario detection using techniques from topic segmentation and text classification."
}