Survey of Abstract Meaning Representation: Then, Now, Future
It provides a comprehensive overview for researchers in natural language processing, but it is incremental as a survey paper.
This survey reviews Abstract Meaning Representation (AMR), a graph-based semantic framework for capturing sentence meaning, and analyzes its parsing, generation, and applications to identify future research directions.
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.