Publication: "When they say weed causes depression, but it's your fav antidepressant": Knowledgeaware attention framework for relationship extraction
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Issued Date
2021-03-01
Resource Type
ISSN
19326203
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2-s2.0-85103338984
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Mahidol University
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SCOPUS
Bibliographic Citation
PLoS ONE. Vol.16, No.3 March (2021)
Suggested Citation
Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth "When they say weed causes depression, but it's your fav antidepressant": Knowledgeaware attention framework for relationship extraction. PLoS ONE. Vol.16, No.3 March (2021). doi:10.1371/journal.pone.0248299 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/79373
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Title
"When they say weed causes depression, but it's your fav antidepressant": Knowledgeaware attention framework for relationship extraction
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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.