Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa

• URL: http://arxiv.org/abs/2006.16146v1
• Date: Mon, 29 Jun 2020 16:10:27 GMT
• Title: Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa
• Authors: Katikapalli Subramanyam Kalyan, S.Sangeetha
• Abstract summary: This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from non ADR tweets and is treated as binary classification. In task 3, we extract ADR mentions and then mapping them to MedDRA codes. Our models achieve promising results in both the tasks with significant improvements over average scores.
• Score: 5.33024001730262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from nonADR tweets and is treated as binary classification. Task3 involves extracting ADR mentions and then mapping them to MedDRA codes. Extracting ADR mentions is treated as sequence labeling and normalizing ADR mentions is treated as multi-class classification. Our system is based on pre-trained language model RoBERTa and it achieves a) F1-score of 58% in task2 which is 12% more than the average score b) relaxed F1-score of 70.1% in ADR extraction of task 3 which is 13.7% more than the average score and relaxed F1-score of 35% in ADR extraction + normalization of task3 which is 5.8% more than the average score. Overall, our models achieve promising results in both the tasks with significant improvements over average scores.

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