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

• URL: http://arxiv.org/abs/2012.04539v1
• Date: Mon, 7 Dec 2020 07:55:31 GMT
• Title: Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT
• Authors: Dylan Whang and Soroush Vosoughi
• Abstract summary: 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.
• Score: 2.1574781022415364
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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