Details

Autor(en) / Beteiligte

• Van Gysel, Jens E. L.
• Vigus, Meagan
• Chun, Jayeol
• Lai, Kenneth
• Moeller, Sarah
• Yao, Jiarui
• O’Gorman, Tim
• Cowell, Andrew
• Croft, William
• Huang, Chu-Ren
• Hajič, Jan
• Martin, James H.
• Oepen, Stephan
• Palmer, Martha
• Pustejovsky, James
• Vallejos, Rosa
• Xue, Nianwen

Titel

Designing a Uniform Meaning Representation for Natural Language Processing

Ist Teil von

• KI. Künstliche Intelligenz (Oldenbourg), 2021-11, Vol.35 (3-4), p.343-360

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In 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.