@inproceedings{chen-palmer-2017-unsupervised,
title = "Unsupervised {AMR}-Dependency Parse Alignment",
author = "Chen, Wei-Te and
Palmer, Martha",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1053",
pages = "558--567",
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.",
}
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%0 Conference Proceedings
%T Unsupervised AMR-Dependency Parse Alignment
%A Chen, Wei-Te
%A Palmer, Martha
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F chen-palmer-2017-unsupervised
%X 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.
%U https://aclanthology.org/E17-1053
%P 558-567
Markdown (Informal)
[Unsupervised AMR-Dependency Parse Alignment](https://aclanthology.org/E17-1053) (Chen & Palmer, EACL 2017)
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.