#GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets
Hanna Varachkina, Stefan Ziehe, Tillmann Dönicke, Franziska Pannach
Abstract
In this system paper, we present a transformer-based approach to the detection of informativeness in English tweets on the topic of the current COVID-19 pandemic. Our models distinguish informative tweets, i.e. tweets containing statistics on recovery, suspected and confirmed cases and COVID-19 related deaths, from uninformative tweets. We present two transformer-based approaches as well as a Naive Bayes classifier and a support vector machine as baseline systems. The transformer models outperform the baselines by more than 0.1 in F1-score, with F1-scores of 0.9091 and 0.9036. Our models were submitted to the shared task Identification of informative COVID-19 English tweets WNUT-2020 Task 2.- Anthology ID:
- 2020.wnut-1.68
- 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:
- 462–465
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.68
- DOI:
- 10.18653/v1/2020.wnut-1.68
- Cite (ACL):
- Hanna Varachkina, Stefan Ziehe, Tillmann Dönicke, and Franziska Pannach. 2020. #GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 462–465, Online. Association for Computational Linguistics.
- Cite (Informal):
- #GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets (Varachkina et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2020.wnut-1.68.pdf
- Data
- WNUT-2020 Task 2