@inproceedings{poot-van-cranenburgh-2020-benchmark,
    title = "A Benchmark of Rule-Based and Neural Coreference Resolution in {D}utch Novels and News",
    author = "Poot, Corb{\`e}n  and
      van Cranenburgh, Andreas",
    editor = "Ogrodniczuk, Maciej  and
      Ng, Vincent  and
      Grishina, Yulia  and
      Pradhan, Sameer",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2020.crac-1.9/",
    pages = "79--90",
    abstract = "We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation differences. The code and models used in this paper are available at \url{https://github.com/andreasvc/crac2020}"
}