Abstract
This paper presents the results of the wet labinformation extraction task at WNUT 2020.This task consisted of two sub tasks- (1) anamed entity recognition task with 13 partic-ipants; and (2) a relation extraction task with2 participants. We outline the task, data an-notation process, corpus statistics, and providea high-level overview of the participating sys-tems for each sub task.- Anthology ID:
- 2020.wnut-1.33
- 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:
- 260–267
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2020.wnut-1.33/
- DOI:
- 10.18653/v1/2020.wnut-1.33
- Cite (ACL):
- Jeniya Tabassum, Wei Xu, and Alan Ritter. 2020. WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 260–267, Online. Association for Computational Linguistics.
- Cite (Informal):
- WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols (Tabassum et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2020.wnut-1.33.pdf
- Code
- jeniyat/WNUT_2020_NER
- Data
- WNUT 2020