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
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 558–567
- Language:
- URL:
- https://aclanthology.org/E17-1053
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/E17-1053.pdf