Ujjwal Verma


2021

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BERT based Transformers lead the way in Extraction of Health Information from Social Media
Sidharth Ramesh | Abhiraj Tiwari | Parthivi Choubey | Saisha Kashyap | Sahil Khose | Kumud Lakara | Nishesh Singh | Ujjwal Verma
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2021 shared tasks. We participated in 2 tasks: (1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms (Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific pre-processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our system obtained an F1-score of 50% with improvements up to +8% F1 over the median score across all submissions. The BERTweet model achieved an F1 score of 94% on SMM4H 2021 Task-6.