Abstract Meaning Representation(AMR),with the ability of accurately abstracting the complete meaning of sentences,realizes domain-independent semantic representation of entire sentences.AMR parsing has an impact on the performance of downstream NLP tasks and becomes a popular research topic both domestically and interna-tionally in recent years.We first employ the CiteSpace tool to analyze the overall research landscape of AMR,revea-ling a much less Chinese AMR parsing researches compared with those for English.Then we discuss the develop-ment of AMR corpus and the difficulties of concept recognition,relation recognition,alignment and integration of structural information in AMR parsing.We categorize AMR parsing into four types,and explore the evolution of AMR parsing methods.Finally,we select 21 English AMR parsers and 7 Chinese AMR parsers,and compare vari-ous experimental metrics including Smatch.
abstract meaning representationparsingcorpusnatural language processing
NETL
NSTL
万方数据
维普
