  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 clinical
notes (Johnson et al., 2016) and PubMed papers (Sayers et al., 2021; Gao et
al., 2020). While the resulting in-domain models have shown promising results
for many biomedical tasks, adverse drug event normalization on social media
texts has so far remained challenging for them (Portelli et al., 2022). In this
paper, we propose a new approach for adverse drug event normalization on social
media relying on general-purpose model initialization via BioLORD (Remy et al.,
2022) and a semantic-text-similarity fine-tuning named STS. Our experimental
results on several social media datasets demonstrate the effectiveness of our
proposed approach, by achieving state-of-the-art performance. Based on its
strong performance across all the tested datasets, we believe this work could
emerge as a turning point for the task of adverse drug event normalization on
social media and has the potential to serve as a benchmark for future research
in the field.
