Abstract Meaning Representation (AMR) is a semantic representation language used to represent the meanings of thousands of English sentences. The authors aim to create a sembank of simple, whole-sentence semantic structures to spur new work in statistical natural language understanding and generation, similar to how the Penn Treebank encouraged statistical parsing. This paper provides an overview of AMR and associated tools. AMR is a rooted, labeled graph that is easy for both humans and programs to read and process. It abstracts away from syntactic idiosyncrasies and assigns the same AMR to sentences with the same basic meaning. AMR uses PropBank framesets and is agnostic about how meanings are derived from strings. It is heavily biased towards English and not an Interlingua. AMR is described in a 50-page annotation guideline. AMR uses a variety of relations to represent different aspects of meaning, including frame arguments, general semantic relations, and relations for quantities, date-entities, and lists. It also includes inverse relations and reifications. AMR concepts include English words, PropBank framesets, and special keywords. The AMR format includes logical representations and various graph notations. AMR is used to represent a wide range of linguistic phenomena, including verbs, nouns, adjectives, prepositions, and named entities. It abstracts away from syntactic structures and focuses on semantic relationships. AMR is agnostic about the relation between strings and meanings, allowing researchers to explore their own ideas about how strings relate to meanings. AMR has limitations, such as not representing inflectional morphology and lacking a universal quantifier. It also does not distinguish between real events and hypothetical ones. AMR has been used to create a sembank of several thousand sentences, with a subset freely downloadable. The authors have developed an AMR Editor for creating and editing AMRs, along with a smatch metric for assessing inter-annotator agreement and automatic AMR parsing accuracy. AMR has been used to annotate a wide range of texts, including newswire, web data, and fiction. AMR has been compared to other semantic representation systems, such as GMB, UCCA, ST, and UNL, in terms of concepts, relations, formalism, granularity, and entities. AMR is a manual annotation system, while GMB and ST produce meaning representations automatically. Future work includes expanding AMR to include more relations, entity normalization, quantification, and temporal relations. The authors also plan to test AMR's ability to canonicalize multiple ways of expressing the same meaning using paraphrase networks. They aim to develop a comprehensive set of abstract frames for various domains.Abstract Meaning Representation (AMR) is a semantic representation language used to represent the meanings of thousands of English sentences. The authors aim to create a sembank of simple, whole-sentence semantic structures to spur new work in statistical natural language understanding and generation, similar to how the Penn Treebank encouraged statistical parsing. This paper provides an overview of AMR and associated tools. AMR is a rooted, labeled graph that is easy for both humans and programs to read and process. It abstracts away from syntactic idiosyncrasies and assigns the same AMR to sentences with the same basic meaning. AMR uses PropBank framesets and is agnostic about how meanings are derived from strings. It is heavily biased towards English and not an Interlingua. AMR is described in a 50-page annotation guideline. AMR uses a variety of relations to represent different aspects of meaning, including frame arguments, general semantic relations, and relations for quantities, date-entities, and lists. It also includes inverse relations and reifications. AMR concepts include English words, PropBank framesets, and special keywords. The AMR format includes logical representations and various graph notations. AMR is used to represent a wide range of linguistic phenomena, including verbs, nouns, adjectives, prepositions, and named entities. It abstracts away from syntactic structures and focuses on semantic relationships. AMR is agnostic about the relation between strings and meanings, allowing researchers to explore their own ideas about how strings relate to meanings. AMR has limitations, such as not representing inflectional morphology and lacking a universal quantifier. It also does not distinguish between real events and hypothetical ones. AMR has been used to create a sembank of several thousand sentences, with a subset freely downloadable. The authors have developed an AMR Editor for creating and editing AMRs, along with a smatch metric for assessing inter-annotator agreement and automatic AMR parsing accuracy. AMR has been used to annotate a wide range of texts, including newswire, web data, and fiction. AMR has been compared to other semantic representation systems, such as GMB, UCCA, ST, and UNL, in terms of concepts, relations, formalism, granularity, and entities. AMR is a manual annotation system, while GMB and ST produce meaning representations automatically. Future work includes expanding AMR to include more relations, entity normalization, quantification, and temporal relations. The authors also plan to test AMR's ability to canonicalize multiple ways of expressing the same meaning using paraphrase networks. They aim to develop a comprehensive set of abstract frames for various domains.