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Biomedical entity linking, also known as biomedical concept normalization,has recently witnessed the rise to prominence of zero-shot contrastive models.However, the pre-training material used for these models has, until now,largely consisted of specialist biomedical content such as MIMIC-III clinicalnotes (Johnson et al., 2016) and PubMed papers (Sayers et al., 2021; Gao etal., 2020). While the resulting in-domain models have shown promising resultsfor many biomedical tasks, adverse drug event normalization on social mediatexts has so far remained challenging for them (Portelli et al., 2022). In thispaper, we propose a new approach for adverse drug event normalization on socialmedia relying on general-purpose model initialization via BioLORD (Remy et al.,2022) and a semantic-text-similarity fine-tuning named STS. Our experimentalresults on several social media datasets demonstrate the effectiveness of ourproposed approach, by achieving state-of-the-art performance. Based on itsstrong performance across all the tested datasets, we believe this work couldemerge as a turning point for the task of adverse drug event normalization onsocial media and has the potential to serve as a benchmark for future researchin the field.

Boosting Adverse Drug Event Normalization on Social Media:  General-Purpose Model Initialization and Biomedical Semantic Text Similarity  Benefit Zero-Shot Linking in Informal Contexts