Research map / Large language models / AI and society
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| swap generic BERT encoder for COVID-TWITTER-BERT (continued pretraining on COVID tweets) | depGCN · METS-CoV-TSA test | overall F1: 59.96 -> 71.39 | Helped |
| swap generic BERT encoder for COVID-TWITTER-BERT | BERT-SPC · METS-CoV-TSA test | overall F1: 59.31 -> 70.03 | Helped |
| swap generic BERT encoder for COVID-TWITTER-BERT | depGCN · METS-CoV-TSA test Drug | F1: 49.32 -> 67.35 | Helped |
| use COVID-TWITTER-BERT instead of RoBERTa-large | NER model · METS-CoV-NER test | overall micro-F1: 82.55 -> 83.88 | Helped |
| use COVID-TWITTER-BERT instead of RoBERTa-large | NER model · METS-CoV-NER test Drug | F1: 86.79 -> 86.35 | Hurt |
| use pretrained transformers instead of BiLSTM-CRF | NER model · METS-CoV-NER test | overall micro-F1: 78.10 -> 82.55 | Helped |
| use BART-large instead of BART-base | NER model · METS-CoV-NER test | overall micro-F1: 80.17 -> 75.56 | Hurt |
| add character CNN and CRF layer to BiLSTM | WLSTM · METS-CoV-NER test | overall micro-F1: 75.02 -> 78.10 | Helped |
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