  This paper outlines the performance evaluation of a system for adverse drug
event normalization, developed by the Data Science for Digital Health (DS4DH)
group for the Social Media Mining for Health Applications (SMM4H) 2023 shared
task 5. Shared task 5 targeted the normalization of adverse drug event mentions
in Twitter to standard concepts of the Medical Dictionary for Regulatory
Activities terminology. Our system hinges on a two-stage approach: BERT
fine-tuning for entity recognition, followed by zero-shot normalization using
sentence transformers and reciprocal-rank fusion. The approach yielded a
precision of 44.9%, recall of 40.5%, and an F1-score of 42.6%. It outperformed
the median performance in shared task 5 by 10% and demonstrated the highest
performance among all participants. These results substantiate the
effectiveness of our approach and its potential application for adverse drug
event normalization in the realm of social media text mining.
