Automatic Extraction of Medication Names in Tweets as Named Entity Recognition

• URL: http://arxiv.org/abs/2111.15641v1
• Date: Tue, 30 Nov 2021 18:25:32 GMT
• Title: Automatic Extraction of Medication Names in Tweets as Named Entity Recognition
• Authors: Carol Anderson, Bo Liu, Anas Abidin, Hoo-Chang Shin, Virginia Adams
• Abstract summary: Biocreative VII Task 3 focuses on mining this information by recognizing mentions of medications and dietary supplements in tweets. We approach this task by fine tuning multiple BERT-style language models to perform token-level classification. Our best system consists of five Megatron-BERT-345M models and achieves a strict F1 score of 0.764 on unseen test data.
• Score: 3.7462395049372894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social media posts contain potentially valuable information about medical conditions and health-related behavior. Biocreative VII Task 3 focuses on mining this information by recognizing mentions of medications and dietary supplements in tweets. We approach this task by fine tuning multiple BERT-style language models to perform token-level classification, and combining them into ensembles to generate final predictions. Our best system consists of five Megatron-BERT-345M models and achieves a strict F1 score of 0.764 on unseen test data.

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