解析语义图谱AMR：从原理到生成与应用的全景综述
Survey of Abstract Meaning Representation: Then, Now, Future
- 用有向无环图表示句子语义，节点为概念，边为关系
- 涵盖文本转AMR解析与AMR转文本生成的主流方法
- 适合语言理解、生成与信息提取研究者参考
本文系统综述抽象语义表示（AMR），一种基于图结构的语义表征框架。AMR将句子建模为根节点出发的有向无环图，节点代表概念，边表示语义关系，有效捕捉复杂句式的深层含义。本综述探讨了AMR及其扩展的能力，重点分析文本到AMR的解析任务与AMR到文本的生成任务，涵盖传统、当前及未来可能的技术路径。同时回顾了AMR在文本生成、文本分类、信息抽取与信息检索等领域的应用。通过梳理最新进展与挑战，为未来研究方向提供了洞见，揭示了AMR在提升机器对人类语言理解能力方面的潜力。
原文摘要 · Abstract (English)
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.
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