Big Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification

Chris Miller, Soroush Vosoughi


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
Bibkey:
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)
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PDF:
https://aclanthology.org/2020.wnut-1.36.pdf