  With strong marketing advocacy of the benefits of cannabis use for improved
mental health, cannabis legalization is a priority among legislators. However,
preliminary scientific research does not conclusively associate cannabis with
improved mental health. In this study, we explore the relationship between
depression and consumption of cannabis in a targeted social media corpus
involving personal use of cannabis with the intent to derive its potential
mental health benefit. We use tweets that contain an association among three
categories annotated by domain experts - Reason, Effect, and Addiction. The
state-of-the-art Natural Langauge Processing techniques fall short in
extracting these relationships between cannabis phrases and the depression
indicators. We seek to address the limitation by using domain knowledge;
specifically, the Drug Abuse Ontology for addiction augmented with Diagnostic
and Statistical Manual of Mental Disorders lexicons for mental health. Because
of the lack of annotations due to the limited availability of the domain
experts' time, we use supervised contrastive learning in conjunction with GPT-3
trained on a vast corpus to achieve improved performance even with limited
supervision. Experimental results show that our method can significantly
extract cannabis-depression relationships better than the state-of-the-art
relation extractor. High-quality annotations can be provided using a nearest
neighbor approach using the learned representations that can be used by the
scientific community to understand the association between cannabis and
depression better.
