Abstract Meaning Representation (AMR) annotation was developed by LDC, SDL/Language Weaver, Inc., the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It is a semantic representation language that captures "who is doing what to whom" in a sentence. Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure. AMR utilizes PropBank frames, non-core semantic roles, within-sentence coreference, named entity annotation, modality, negation, questions, quantities, and so on to represent the semantic structure of a sentence largely independent of its syntax.
LDC’s Catalog contains three cumulative English AMR publications: Release 1.0 (LDC2014T12 [2]), Release 2.0 (LDC2017T10 [3]), and Release 3.0 (LDC2020T02 [4]). The combined result in AMR 3.0 is a semantic treebank of roughly 59,255 English natural language sentences from broadcast conversations, newswire, weblogs, web discussion forums, fiction and web text and includes multi-sentence annotations.
LDC has also published Chinese Abstract Meaning Representation 1.0 (LDC2019T07 [5]) and 2.0 (LDC2021T13 [6]) developed by Brandeis University and Nanjing Normal University. These corpora contain AMR annotations for approximately 20,000 sentences from Chinese Treebank 8.0 (LDC2013T21 [7]). Chinese AMR follows the basic principles developed for English, making adaptations where necessary to accommodate Chinese phenomena.
Abstract Meaning Representation 2.0 - Four Translations (LDC2020T07 [8]), developed by the University of Edinburgh, School of Informatics, consists of Spanish, German, Italian and Chinese Mandarin translations of a subset of sentences from AMR 2.0.
Visit LDC’s Catalog [9]for more details about these publications.