A Short Review of Abstract Meaning Representation Applications | ||
| Journal of Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 2، شماره 3 - شماره پیاپی 9، زمستان 2022، صفحه 1-9 اصل مقاله (703.18 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2023.28930.1120 | ||
| نویسندگان | ||
| Nasim Tohidi؛ Chitra Dadkhah* | ||
| Artificial Engineering Departement, Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Abstract Meaning Representation؛ Application؛ Natural Language Processing؛ Text؛ Semantic | ||
| مراجع | ||
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