  Adverse drug events (ADEs) are unexpected incidents caused by the
administration of a drug or medication. To identify and extract these events,
we require information about not just the drug itself but attributes describing
the drug (e.g., strength, dosage), the reason why the drug was initially
prescribed, and any adverse reaction to the drug. This paper explores the
relationship between a drug and its associated attributes using relation
extraction techniques. We explore three approaches: a rule-based approach, a
deep learning-based approach, and a contextualized language model-based
approach. We evaluate our system on the n2c2-2018 ADE extraction dataset. Our
experimental results demonstrate that the contextualized language model-based
approach outperformed other models overall and obtain the state-of-the-art
performance in ADE extraction with a Precision of 0.93, Recall of 0.96, and an
$F_1$ score of 0.94; however, for certain relation types, the rule-based
approach obtained a higher Precision and Recall than either learning approach.
