coref-data/litbank_raw

Citation text from the card

@inproceedings{bamman-etal-2019-annotated,
    title = "An annotated dataset of literary entities",
    author = "Bamman, David  and
      Popat, Sejal  and
      Shen, Sheng",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1220",
    doi = "10.18653/v1/N19-1220",
    pages = "2138--2144",
    abstract = "We present a new dataset comprised of 210,532 tokens evenly drawn from 100 different English-language literary texts annotated for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle). These categories include non-named entities (such as {``}the boy{''}, {``}the kitchen{''}) and nested structure (such as [[the cook]{'}s sister]). In contrast to existing datasets built primarily on news (focused on geo-political entities and organizations), literary texts offer strikingly different distributions of entity categories, with much stronger emphasis on people and description of settings. We present empirical results demonstrating the performance of nested entity recognition models in this domain; training natively on in-domain lit

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