Name
Title | ||
|---|---|---|
Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.18653/v1/2020.wnut-1.72 | W-NUT@EMNLP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dylan Whang | 1 | 0 | 0.34 |
| soroush vosoughi | 2 | 50 | 9.78 |