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With the increasing legalization of medical and recreational use of cannabis,more research is needed to understand the association between depression andconsumer behavior related to cannabis consumption. Big social media data haspotential to provide deeper insights about these associations to public healthanalysts. In this interdisciplinary study, we demonstrate the value ofincorporating domain-specific knowledge in the learning process to identify therelationships between cannabis use and depression. We develop an end-to-endknowledge infused deep learning framework (Gated-K-BERT) that leverages thepre-trained BERT language representation model and domain-specific declarativeknowledge source (Drug Abuse Ontology (DAO)) to jointly extract entities andtheir relationship using gated fusion sharing mechanism. Our model is furthertailored to provide more focus to the entities mention in the sentence throughentity-position aware attention layer, where ontology is used to locate thetarget entities position. Experimental results show that inclusion of theknowledge-aware attentive representation in association with BERT can extractthe cannabis-depression relationship with better coverage in comparison to thestate-of-the-art relation extractor.

"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction