"When they say weed causes depression, but it's your fav antidepressant": Knowledgeaware attention framework for relationship extraction

Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth

Research output: Contribution to journalArticlepeer-review

Abstract

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-toend knowledge infused deep learning framework (Gated-K-BERT) that leverages the pretrained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology) 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.

Original languageEnglish (US)
Article numbere0248299
JournalPloS one
Volume16
Issue number3 March
DOIs
StatePublished - Mar 2021

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Fingerprint Dive into the research topics of '"When they say weed causes depression, but it's your fav antidepressant": Knowledgeaware attention framework for relationship extraction'. Together they form a unique fingerprint.

Cite this