Abstract
We describe the systems developed for the WNUT-2020 shared task 2, identification of informative COVID-19 English Tweets. BERT is a highly performant model for Natural Language Processing tasks. We increased BERT’s performance in this classification task by fine-tuning BERT and concatenating its embeddings with Tweet-specific features and training a Support Vector Machine (SVM) for classification (henceforth called BERT+). We compared its performance to a suite of machine learning models. We used a Twitter specific data cleaning pipeline and word-level TF-IDF to extract features for the non-BERT models. BERT+ was the top performing model with an F1-score of 0.8713.- Anthology ID:
- 2020.wnut-1.72
- 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:
- 480–484
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2020.wnut-1.72/
- DOI:
- 10.18653/v1/2020.wnut-1.72
- Cite (ACL):
- Dylan Whang and Soroush Vosoughi. 2020. Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 480–484, Online. Association for Computational Linguistics.
- Cite (Informal):
- Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT (Whang & Vosoughi, WNUT 2020)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2020.wnut-1.72.pdf