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Adverse Drug Event (ADE) extraction models can rapidly examine largecollections of social media texts, detecting mentions of drug-related adversereactions and trigger medical investigations. However, despite the recentadvances in NLP, it is currently unknown if such models are robust in face ofnegation, which is pervasive across language varieties.  In this paper we evaluate three state-of-the-art systems, showing theirfragility against negation, and then we introduce two possible strategies toincrease the robustness of these models: a pipeline approach, relying on aspecific component for negation detection; an augmentation of an ADE extractiondataset to artificially create negated samples and further train the models.  We show that both strategies bring significant increases in performance,lowering the number of spurious entities predicted by the models. Our datasetand code will be publicly released to encourage research on the topic.

NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations