@inproceedings{pustejovsky-etal-2019-modeling,
    title = "Modeling Quantification and Scope in {A}bstract {M}eaning {R}epresentations",
    author = "Pustejovsky, James  and
      Lai, Ken  and
      Xue, Nianwen",
    editor = "Xue, Nianwen  and
      Croft, William  and
      Hajic, Jan  and
      Huang, Chu-Ren  and
      Oepen, Stephan  and
      Palmer, Martha  and
      Pustejovksy, James",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W19-3303/",
    doi = "10.18653/v1/W19-3303",
    pages = "28--33",
    abstract = "In this paper, we propose an extension to Abstract Meaning Representations (AMRs) to encode scope information of quantifiers and negation, in a way that overcomes the semantic gaps of the schema while maintaining its cognitive simplicity. Specifically, we address three phenomena not previously part of the AMR specification: quantification, negation (generally), and modality. The resulting representation, which we call {\textquotedblleft}Uniform Meaning Representation{\textquotedblright} (UMR), adopts the predicative core of AMR and embeds it under a {\textquotedblleft}scope{\textquotedblright} graph when appropriate. UMR representations differ from other treatments of quantification and modal scope phenomena in two ways: (a) they are more transparent; and (b) they specify default scope when possible.{\textquoteleft}"
}