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Mapping AMR to UMR: Resources for Adapting Existing Corpora for Cross-Lingual Compatibility

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Author
Bonn, Julia
Myers, Skatje
Van Gysel, Jens E.L.
Denk, Lukas
Vigus, Meagan
Zhao, Jin
Cowell, Andrew
Croft, William
Hajič, JanORCiD Profile - 0000-0002-3503-7730WoS Profile - D-3429-2017Scopus Profile - 6602292051
Martin, James H.
Palmer, Alexis
Palmer, Martha
Pustejovsky, James
Urešová, ZdeňkaORCiD Profile - 0000-0001-9288-8139WoS Profile - M-5070-2017Scopus Profile - 26665152300
Vallejos, Rosa
Xue, Nianwen
Association for Computational Linguistics

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Publication date
2023
Published in
TLT 2023 - 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023), Proceedings of the Conference
Publisher / Publication place
Association for Computational Linguistics (Washington, D.C., USA)
Volume / Issue
21
ISBN / ISSN
ISBN: 978-1-959429-33-3
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  • Faculty of Mathematics and Physics
Abstract
This paper presents detailed mappings between the structures used in Abstract Meaning Representation (AMR) and those used in Uniform Meaning Representation (UMR). These structures include general semantic roles, rolesets, and concepts that are largely shared between AMR and UMR, but with crucial differences. While UMR annotation of new low-resource languages is ongoing, AMR-annotated corpora already exist for many languages, and these AMR corpora are ripe for conversion to UMR format. Rather than focusing on semantic coverage that is new to UMR (which will likely need to be dealt with manually), this paper serves as a resource (with illustrated mappings) for users looking to understand the fine-grained adjustments that have been made to the representation techniques for semantic categories present in both AMR and UMR.
Keywords
abstract meaning representation, uniform meaning representation, corpus, semantics
Permanent link
https://hdl.handle.net/20.500.14178/2335
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Full text of this result is licensed under: Creative Commons Uveďte původ 4.0 International

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