ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain
Biaoyan Fang, Christian Druckenbrodt, Saber A Akhondi, Jiayuan He, Timothy Baldwin, Karin Verspoor
Abstract
Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.- Anthology ID:
- 2021.eacl-main.116
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1362–1375
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.116
- DOI:
- 10.18653/v1/2021.eacl-main.116
- Cite (ACL):
- Biaoyan Fang, Christian Druckenbrodt, Saber A Akhondi, Jiayuan He, Timothy Baldwin, and Karin Verspoor. 2021. ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1362–1375, Online. Association for Computational Linguistics.
- Cite (Informal):
- ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain (Fang et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.eacl-main.116.pdf
- Code
- biaoyanf/chemu-ref