An Incremental Parser for Abstract Meaning Representation

Marco Damonte, Shay B. Cohen, Giorgio Satta


Abstract
Abstract Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.
Anthology ID:
E17-1051
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
536–546
Language:
URL:
https://aclanthology.org/E17-1051
DOI:
Bibkey:
Cite (ACL):
Marco Damonte, Shay B. Cohen, and Giorgio Satta. 2017. An Incremental Parser for Abstract Meaning Representation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 536–546, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
An Incremental Parser for Abstract Meaning Representation (Damonte et al., EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-1051.pdf
Code
 mdtux89/amr-evaluation +  additional community code