  With the increasing legalization of medical and recreational use of cannabis,
more research is needed to understand the association between depression and
consumer behavior related to cannabis consumption. Big social media data has
potential to provide deeper insights about these associations to public health
analysts. In this interdisciplinary study, we demonstrate the value of
incorporating domain-specific knowledge in the learning process to identify the
relationships between cannabis use and depression. We develop an end-to-end
knowledge infused deep learning framework (Gated-K-BERT) that leverages the
pre-trained BERT language representation model and domain-specific declarative
knowledge source (Drug Abuse Ontology (DAO)) to jointly extract entities and
their relationship using gated fusion sharing mechanism. Our model is further
tailored to provide more focus to the entities mention in the sentence through
entity-position aware attention layer, where ontology is used to locate the
target entities position. Experimental results show that inclusion of the
knowledge-aware attentive representation in association with BERT can extract
the cannabis-depression relationship with better coverage in comparison to the
state-of-the-art relation extractor.
