@inproceedings{meyers-etal-2025-semantic,
    title = "Semantic Role Labeling of {N}om{B}ank Partitives",
    author = "Meyers, Adam  and
      Savant, Advait Pravin  and
      Ortega, John E.",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.23/",
    pages = "324--336",
    abstract = "This article is about Semantic Role Labeling for English partitive nouns (5{\%}/REL of the price/ARG1; The price/ARG1 rose 5 percent/REL) in the NomBank annotated corpus. Several systems are described using traditional and transformer-based machine learning, as well as ensembling. Our highest scoring system achieves an F1 of 91.74{\%} using ``gold'' parses from the Penn Treebank and 91.12{\%} when using the Berkeley Neural parser. This research includes both classroom and experimental settings for system development."
}