Abstract
This paper presents a survey of Abstract Meaning Representation (AMR), a semantic representation framework that captures the meaning of sentences through a graph-based structure. AMR represents sentences as rooted, directed acyclic graphs, where nodes correspond to concepts and edges denote relationships, effectively encoding the meaning of complex sentences. This survey investigates AMR and its extensions, focusing on AMR capabilities. It then explores the parsing (text-to-AMR) and generation (AMR-to-text) tasks by showing traditional, current, and possible futures approaches. It also reviews various applications of AMR including text generation, text classification, and information extraction and information seeking. By analyzing recent developments and challenges in the field, this survey provides insights into future directions for research and the potential impact of AMR on enhancing machine understanding of human language.
Abstract (translated)
本文对抽象意义表示（AMR）进行了一次综述，这是一种通过基于图的结构来捕捉句子含义的语义表示框架。在AMR中，句子被表示为以根节点为起点的有向无环图，其中节点代表概念，边则表示关系，从而有效地编码了复杂句子的意义。本综述探讨了AMR及其扩展，并重点关注其功能。接着，它通过展示传统、当前及可能未来的方法来探索解析（文本到AMR）和生成（AMR到文本）任务。此外，本文还回顾了AMR的各种应用，包括文本生成、文本分类以及信息抽取和信息检索。通过对该领域的近期发展与挑战进行分析，本综述为未来的研究方向提供了洞见，并探讨了AMR在增强机器对人类语言理解的潜在影响。
URL
https://arxiv.org/abs/2505.03229
