@inproceedings{bonn-etal-2024-building-broad,
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Building a Broad Infrastructure for Uniform Meaning Representations
%A Bonn, Julia
%A Buchholz, Matthew J.
%A Chun, Jayeol
%A Cowell, Andrew
%A Croft, William
%A Denk, Lukas
%A Ge, Sijia
%A Hajič, Jan
%A Lai, Kenneth
%A Martin, James H.
%A Myers, Skatje
%A Palmer, Alexis
%A Palmer, Martha
%A Post, Claire Benet
%A Pustejovsky, James
%A Stenzel, Kristine
%A Sun, Haibo
%A Urešová, Zdeňka
%A Vallejos, Rosa
%A Van Gysel, Jens E. L.
%A Vigus, Meagan
%A Xue, Nianwen
%A Zhao, Jin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F bonn-etal-2024-building-broad
%X 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.
%U https://aclanthology.org/2024.lrec-main.229
%P 2537-2547
Markdown (Informal)
[Building a Broad Infrastructure for Uniform Meaning Representations](https://aclanthology.org/2024.lrec-main.229) (Bonn et al., LREC-COLING 2024)
ACL
- Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, et al.. 2024. Building a Broad Infrastructure for Uniform Meaning Representations. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2537–2547, Torino, Italia. ELRA and ICCL.