Unsupervised AMR-Dependency Parse Alignment

Wei-Te Chen, Martha Palmer


Abstract
In this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and our aligner improves current AMR parser performance. Our aligner involves several different features, including named entity tags and semantic role labels, and uses Expectation-Maximization training. Results show that our aligner reaches an 87.1% F-Score score with the experimental data, and enhances AMR parsing.
Anthology ID:
E17-1053
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:
558–567
Language:
URL:
https://aclanthology.org/E17-1053
DOI:
Bibkey:
Cite (ACL):
Wei-Te Chen and Martha Palmer. 2017. Unsupervised AMR-Dependency Parse Alignment. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 558–567, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Unsupervised AMR-Dependency Parse Alignment (Chen & Palmer, EACL 2017)
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PDF:
https://aclanthology.org/E17-1053.pdf