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        <title>Core Semantic First: A Top-down Approach for AMR Parsing</title>
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    <abstract>We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The core semantic first principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.</abstract>
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%0 Conference Proceedings
%T Core Semantic First: A Top-down Approach for AMR Parsing
%A Cai, Deng
%A Lam, Wai
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F cai-lam-2019-core
%X We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The core semantic first principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.
%R 10.18653/v1/D19-1393
%U https://aclanthology.org/D19-1393
%U https://doi.org/10.18653/v1/D19-1393
%P 3799-3809

Markdown (Informal)

[Core Semantic First: A Top-down Approach for AMR Parsing](https://aclanthology.org/D19-1393) (Cai & Lam, EMNLP-IJCNLP 2019)

• Core Semantic First: A Top-down Approach for AMR Parsing (Cai & Lam, EMNLP-IJCNLP 2019)

ACL

• Deng Cai and Wai Lam. 2019. Core Semantic First: A Top-down Approach for AMR Parsing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3799–3809, Hong Kong, China. Association for Computational Linguistics.