mlsanityData to 7 Oct 2026

"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

arXiv 2102.01222 ↗First posted 1 Feb 2021cs.CL cs.AIcited by 7

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Abstract (arXiv)

With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators. However, preliminary scientific research does not conclusively associate cannabis with improved mental health. In this study, we explore the relationship between depression and consumption of cannabis in a targeted social media corpus involving personal use of cannabis with the intent to derive its potential mental health benefit. We use tweets that contain an association among three categories annotated by domain experts - Reason, Effect, and Addiction. The state-of-the-art Natural Langauge Processing techniques fall short in extracting these relationships between cannabis phrases and the depression indicators. We seek to address the limitation by using domain knowledge; specifically, the Drug Abuse Ontology for addiction augmented with Diagnostic and Statistical Manual of Mental Disorders lexicons for mental health. Because of the lack of annotations due to the limited availability of the domain experts' time, we use supervised contrastive learning in conjunction with GPT-3 trained on a vast corpus to achieve improved performance even with limited supervision. Experimental results show that our method can significantly extract cannabis-depression relationships better than the state-of-the-art relation extractor. High-quality annotations can be provided using a nearest neighbor approach using the learned representations that can be used by the scientific community to understand the association between cannabis and depression better.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 55 references that are in the map. First posted on arXiv 1 Feb 2021.

Builds on

23 of 55 references.

References · 23

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11 more
  1. 2019Extracting Multiple-Relations in One-Pass with Pre-Trained Transformerscited by 19
  2. 2019Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Textcited by 19
  3. 2019Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learningcited by 19
  4. 2019Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typingcited by 13
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Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 4.

7 papers.

  1. 2021NLP is Not enough -- Contextualization of User Input in Chatbotscited by 6
  2. 2022KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contextscited by 5
  3. 2023Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Casecited by 4
  4. 2021eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarketcited by 3
  5. 2023Knowledge-Infused Self Attention Transformerscited by 3
  6. 2023IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representationscited by 3
  7. 2023"Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkwebcited by 0