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Designing a Uniform Meaning Representation for Natural Language Processing

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dc.contributor.authorVan Gysel, Jens E. L.
dc.contributor.authorVigus, Meagan
dc.contributor.authorChun, Jayeol
dc.contributor.authorLai, Kenneth
dc.contributor.authorMoeller, Sarah
dc.contributor.authorYao, Jiarui
dc.contributor.authorO’Gorman, Tim
dc.contributor.authorCowell, Andrew
dc.contributor.authorCroft, William
dc.contributor.authorHuang, Chu-Ren
dc.contributor.authorHajič, Jan
dc.contributor.authorMartin, James H.
dc.contributor.authorOepen, Stephan
dc.contributor.authorPalmer, Martha
dc.contributor.authorPustejovsky, James
dc.contributor.authorVallejos, Rosa
dc.contributor.authorXue, Nianwen
dc.date.accessioned2021-12-16T13:23:00Z
dc.date.available2021-12-16T13:23:00Z
dc.date.issued2021
dc.description.abstractIn this paper we present Uniform Meaning Representation (UMR), a meaning representation designed to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed for English, but also draws from other meaning representations. UMR extends AMR to other languages, particularly morphologically complex, low-resource languages. UMR also adds features to AMR that are critical to semantic interpretation and enhances AMR by proposing a companion document-level representation that captures linguistic phenomena such as coreference as well as temporal and modal dependencies that potentially go beyond sentence boundaries.de
dc.identifier.doi10.1007/s13218-021-00722-w
dc.identifier.pissn1610-1987
dc.identifier.urihttp://dx.doi.org/10.1007/s13218-021-00722-w
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37809
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 35, No. 0
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectMeaning representation
dc.subjectNatural language processing
dc.titleDesigning a Uniform Meaning Representation for Natural Language Processingde
dc.typeText/Journal Article
gi.citation.endPage360
gi.citation.startPage343

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Künstliche Intelligenz 35(3-4) - Oktober 2021