A Short Review of Abstract Meaning Representation Applications
Publish place: Journal of Modeling & Simulation in Electrical & Electronics Engineering، Vol: 2، Issue: 3
Publish Year: 1401
Type: Journal paper
Language: English
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JR_MSEEE-2-3_001
Index date: 23 September 2024
A Short Review of Abstract Meaning Representation Applications abstract
Abstract Meaning Representation (AMR) is a representation model in which AMRs are rooted and labeled graphs that capture semantics on the sentence level while abstracting away from Morpho-Syntactic properties. The nodes of the graph represent meaning concepts and the edge labels show relationships between them. The application of AMR, as a principal form of structured sentence semantics, in Natural Language Processing (NLP) tasks is widely increasing, and it is considered a turning point for NLP research. The present study gives a brief review of the existing AMR applications in various NLP tasks. Moreover, they are compared and some of their basic features are discussed.
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A Short Review of Abstract Meaning Representation Applications authors
Nasim Tohidi
Artificial Engineering Departement, Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
Chitra Dadkhah
Artificial Engineering Departement, Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
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