Abstract
Relation and event extraction is an important task in natural language processing. We introduce a system which uses contextualized knowledge graph completion to classify relations and events between known entities in a noisy text environment. We report results which show that our system is able to effectively extract relations and events from a dataset of wet lab protocols.- Anthology ID:
- 2020.wnut-1.36
- Volume:
- Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 281–285
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.36
- DOI:
- 10.18653/v1/2020.wnut-1.36
- Cite (ACL):
- Chris Miller and Soroush Vosoughi. 2020. Big Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 281–285, Online. Association for Computational Linguistics.
- Cite (Informal):
- Big Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification (Miller & Vosoughi, WNUT 2020)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2020.wnut-1.36.pdf