From Syntax to Semantics: Introducing UMR for NLP Annotation

Adriana S. Pagano, Magali Sanches Duran, Federica Gamba


Abstract
Uniform Meaning Representation (UMR) is a cross-linguistic semantic representation framework designed to encode sentence meaning in a structured and interpretable way. Building on the foundations of Abstract Meaning Representation (AMR), UMR extends semantic coverage to events, participants, semantic roles, temporal/aspectual information, modality, and discourse links. It is language-agnostic and therefore suitable for multilingual exploration.This tutorial provides a beginner’s introduction to UMR aimed at an audience with no prior experience with AMR, UMR, or meaning representations. The tutorial begins with a simple introduction to the essentials of Universal Dependencies (UD) needed to understand how UMR graphs can be constructed from syntactic information. Using simple Portuguese examples, the tutorial illustrates how basic UD structures guide the creation of UMR graphs. Participants will leave with a foundational understanding of what UMR is; how it relates to syntax and semantic roles; how to create minimal UMR graphs, and how Portuguese UD treebanks can support UMR annotation.
Anthology ID:
2026.propor-2.41
Volume:
Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2
Month:
April
Year:
2026
Address:
Salvador, Brazil
Editors:
Marlo Souza, Iria de-Dios-Flores, Diana Santos, Larissa Freitas, Jackson Wilke da Cruz Souza, Eugénio Ribeiro
Venue:
PROPOR
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
312
Language:
URL:
https://aclanthology.org/2026.propor-2.41/
DOI:
Bibkey:
Cite (ACL):
Adriana S. Pagano, Magali Sanches Duran, and Federica Gamba. 2026. From Syntax to Semantics: Introducing UMR for NLP Annotation. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2, pages 312–312, Salvador, Brazil. Association for Computational Linguistics.
Cite (Informal):
From Syntax to Semantics: Introducing UMR for NLP Annotation (Pagano et al., PROPOR 2026)
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PDF:
https://aclanthology.org/2026.propor-2.41.pdf