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In recent years, Internet users are reporting Adverse Drug Events (ADE) onsocial media, blogs and health forums. Because of the large volume of reports,pharmacovigilance is seeking to resort to NLP to monitor these outlets. Wepropose for the first time the use of the SpanBERT architecture for the task ofADE extraction: this new version of the popular BERT transformer showedimproved capabilities with multi-token text spans. We validate our hypothesiswith experiments on two datasets (SMM4H and CADEC) with different texttypologies (tweets and blog posts), finding that SpanBERT combined with a CRFoutperforms all the competitors on both of them.

Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies