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Building a Broad Infrastructure for Uniform Meaning Representations
Conference proceeding

Building a Broad Infrastructure for Uniform Meaning Representations

Julia Bonn, Matthew Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajic, Kenneth Lai, James H. Martin, …
PROCEEDINGS OF THE 2024 JOINT INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, LANGUAGE RESOURCES AND EVALUATION, LREC-COLING 2024, pp.2537-2547
International Conference on Computational Linguistics Language Resources and Evaluation
01/01/2024
Handle:
https://hdl.handle.net/10192/79011

Abstract

Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Language & Linguistics Linguistics Science & Technology Computer Science Social Sciences Technology
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, and 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 differences across languages; 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 individual 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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