@inproceedings{bonn-etal-2024-building,
    title = "Building a Broad Infrastructure for Uniform Meaning Representations",
    author = "Bonn, Julia  and
      Buchholz, Matthew J.  and
      Chun, Jayeol  and
      Cowell, Andrew  and
      Croft, William  and
      Denk, Lukas  and
      Ge, Sijia  and
      Haji{\v{c}}, Jan  and
      Lai, Kenneth  and
      Martin, James H.  and
      Myers, Skatje  and
      Palmer, Alexis  and
      Palmer, Martha  and
      Post, Claire Benet  and
      Pustejovsky, James  and
      Stenzel, Kristine  and
      Sun, Haibo  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka  and
      Vallejos, Rosa  and
      Van Gysel, Jens E. L.  and
      Vigus, Meagan  and
      Xue, Nianwen  and
      Zhao, Jin",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.229",
    pages = "2537--2547",
    abstract = "This paper reports the first release of the UMR (Uniform Meaning Representation) data set. UMR is a graph-based meaning representation formalism consisting of a sentence-level graph and a document-level graph. The sentence-level graph represents predicate-argument structures, named entities, word senses, aspectuality of events, as well as person and number information for entities. The document-level graph represents coreferential, temporal, and modal relations that go beyond sentence boundaries. UMR is designed to capture the commonalities and variations across languages and this is done through the use of a common set of abstract concepts, relations, and attributes as well as concrete concepts derived from words from invidual languages. This UMR release includes annotations for six languages (Arapaho, Chinese, English, Kukama, Navajo, Sanapana) that vary greatly in terms of their linguistic properties and resource availability. We also describe on-going efforts to enlarge this data set and extend it to other genres and modalities. We also briefly describe the available infrastructure (UMR annotation guidelines and tools) that others can use to create similar data sets.",
}

This paper reports the first release of the UMR (Uniform Meaning Representation) data set. UMR is a graph-based meaning representation formalism consisting of a sentence-level graph and a document-level graph. The sentence-level graph represents predicate-argument structures, named entities, word senses, aspectuality of events, as well as person and number information for entities. The document-level graph represents coreferential, temporal, and modal relations that go beyond sentence boundaries. UMR is designed to capture the commonalities and variations across languages and this is done through the use of a common set of abstract concepts, relations, and attributes as well as concrete concepts derived from words from invidual languages. This UMR release includes annotations for six languages (Arapaho, Chinese, English, Kukama, Navajo, Sanapana) that vary greatly in terms of their linguistic properties and resource availability. We also describe on-going efforts to enlarge this data set and extend it to other genres and modalities. We also briefly describe the available infrastructure (UMR annotation guidelines and tools) that others can use to create similar data sets.
Bonn, Julia Buchholz, Matthew J. Chun, Jayeol Cowell, Andrew Croft, William Denk, Lukas Ge, Sijia Haji{\v{c}}, Jan Lai, Kenneth Martin, James H. Myers, Skatje Palmer, Alexis Palmer, Martha Post, Claire Benet Pustejovsky, James Stenzel, Kristine Sun, Haibo Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka Vallejos, Rosa Van Gysel, Jens E. L. Vigus, Meagan Xue, Nianwen Zhao, Jin
Building a Broad Infrastructure for Uniform Meaning Representations