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arXiv · 2012.04539

Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT

Dylan Whang·Soroush Vosoughi
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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.

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Dylan Whang, Soroush Vosoughi. 2020-12-07. Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT. https://doi.org/10.18653/v1/2020.wnut-1.72

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