#GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets
2020 | conference paper. A publication with affiliation to the University of Göttingen.
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#GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets
Varachkina, H. ; Ziehe, S.; Dönicke, T. & Pannach, F. (2020)
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020) pp. 462-465. Sixth Workshop on Noisy User-generated Text (W-NUT 2020), Online. DOI: https://doi.org/10.18653/v1/2020.wnut-1.68
Documents & Media
Details
- Authors
- Varachkina, Hanna ; Ziehe, Stefan; Dönicke, Tillmann ; Pannach, Franziska
- 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.
- Issue Date
- 2020
- Conference
- Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
- Conference Place
- Online
- Event start
- 2020-11
- Event end
- 2020-11
- Language
- English
