"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction.
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 pub...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248299&type=printable |
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| author | Shweta Yadav Usha Lokala Raminta Daniulaityte Krishnaprasad Thirunarayan Francois Lamy Amit Sheth |
| author_facet | Shweta Yadav Usha Lokala Raminta Daniulaityte Krishnaprasad Thirunarayan Francois Lamy Amit Sheth |
| author_sort | Shweta Yadav |
| collection | DOAJ |
| description | 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) 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. |
| format | Article |
| id | doaj-art-a35208076ed6491083aab419c19b2b09 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-a35208076ed6491083aab419c19b2b092025-08-20T02:00:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024829910.1371/journal.pone.0248299"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction.Shweta YadavUsha LokalaRaminta DaniulaityteKrishnaprasad ThirunarayanFrancois LamyAmit ShethWith 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) 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248299&type=printable |
| spellingShingle | Shweta Yadav Usha Lokala Raminta Daniulaityte Krishnaprasad Thirunarayan Francois Lamy Amit Sheth "When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction. PLoS ONE |
| title | "When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction. |
| title_full | "When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction. |
| title_fullStr | "When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction. |
| title_full_unstemmed | "When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction. |
| title_short | "When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction. |
| title_sort | when they say weed causes depression but it s your fav antidepressant knowledge aware attention framework for relationship extraction |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248299&type=printable |
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