Summary of Shayona@smm4h23: Covid-19 Self Diagnosis Classification Using Bert and Lightgbm Models, by Rushi Chavda et al.
Shayona@SMM4H23: COVID-19 Self diagnosis classification using BERT and LightGBM models
by Rushi Chavda, Darshan Makwana, Vraj Patel, Anupam Shukla
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents the approaches and results of Team Shayona in Shared Tasks 1 and 4 of SMMH4-23, a binary classification challenge involving COVID-19 diagnosis prediction on English tweets and social anxiety disorder diagnosis prediction on Reddit posts. The team achieved the highest F1-score of 0.94 in Task-1, leveraging the Transformer model (BERT) in combination with LightGBM for both tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper describes how researchers used a combination of AI models to predict whether people reported having COVID-19 or social anxiety disorder based on their social media posts. The team was very good at this task and got the best results among all participants. They used special AI models like BERT to help with the predictions. |
Keywords
» Artificial intelligence » Bert » Classification » F1 score » Transformer
