Abstract
We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.- Anthology ID:
- 2021.acl-long.557
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7167–7182
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.557
- DOI:
- 10.18653/v1/2021.acl-long.557
- Cite (ACL):
- Tianze Shi and Lillian Lee. 2021. Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7167–7182, Online. Association for Computational Linguistics.
- Cite (Informal):
- Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction (Shi & Lee, ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.557.pdf
- Code
- tzshi/bubble-parser-acl21
- Data
- GENIA, Penn Treebank, Universal Dependencies