en عمومى General كاربردي Review paper <b><span style="font-size:9.0pt"><span style="line-height:107%"><span new="" roman="" style="font-family:" times=""><span style="letter-spacing:-.1pt">The application of Abstract Meaning Representation (AMR) is widely increasing as a principal form of structured sentence semantics, and it is considered as a turning point for Natural Language Processing (NLP) research. AMRs are rooted and labeled graphs, which capture semantics on sentence level and abstract away from Morpho-Syntactic properties. The nodes of the graph represent meaning concepts, and the edge labels show relationships between them. In this paper, we give a brief review about the existing approaches of generating text from AMR and parsing input text to produce AMR by studying various research from 2013 to 2022. Besides, we explain how the researchers have been used AMR for prevalent NLP tasks. Afterwards, we describe the existing datasets and evaluation metrics, which can be used in this regard. Finally, we discuss some basic features and challenges of AMR.</span></span></span></span></b> Abstract Meaning Representation, Generation, Parsing, Natural Language Processing, Text 0 0 http://jocee.kntu.ac.ir/browse.php?a_code=A-10-208-1&slc_lang=en&sid=1 Nasim Tohidi n.tohidi@email.kntu.ac.ir 1003194753284600367 1003194753284600367 No Artificial Engineering Depatement, Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran Chitra Dadkhah dadkhah@kntu.ac.ir 1003194753284600368 1003194753284600368 Yes Artificial Engineering Depatement, Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran Alexander Gelbukh gelbukh@cic.ipn.mx 1003194753284600369 1003194753284600369 No CIC, Instituto Politécnico Nacional