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abstract meaning representation: a state-of-the-art review
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نویسنده
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tohidi nasim ,dadkhah chitra ,gelbukh alexander
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منبع
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كنترل - 2024 - دوره : 18 - شماره : 1 - صفحه:13 -43
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چکیده
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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.
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کلیدواژه
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abstract meaning representation ,generation ,parsing ,natural language processing ,text
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آدرس
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k. n. toosi university of technology, faculty of computer engineering, artificial engineering department, iran, k. n. toosi university of technology, faculty of computer engineering, artificial engineering department, iran, instituto politécnico nacional, مکزیک
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پست الکترونیکی
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gelbukh@cic.ipn.mx
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abstract meaning representation: a state-of-the-art review
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Authors
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tohidi nasim ,dadkhah chitra ,gelbukh alexander
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Abstract
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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.
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Keywords
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abstract meaning representation ,generation ,parsing ,natural language processing ,text
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