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

arXiv: https://arxiv.org/abs/2102.01222 · HTML: https://mlsanity.com/paper/2102.01222/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Attention Guided Graph Convolutional Networks for Relation Extraction](https://mlsanity.com/paper/1906.07510/) (2019)
- [Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?](https://mlsanity.com/paper/2010.08660/) (2020)
- [Adversarial training for multi-context joint entity and relation extraction](https://mlsanity.com/paper/1808.06876/) (2018)
- [Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs](https://mlsanity.com/paper/1705.09189/) (2017)
- [Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers](https://mlsanity.com/paper/1902.01030/) (2019)
- [Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text](https://mlsanity.com/paper/1908.07721/) (2019)
- [Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning](https://mlsanity.com/paper/1912.00512/) (2019)
- [Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing](https://mlsanity.com/paper/1901.08163/) (2019)
- ["When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction](https://mlsanity.com/paper/2009.10155/) (2020)
- [Deep learning for extracting protein-protein interactions from biomedical literature](https://mlsanity.com/paper/1706.01556/) (2017)
- [Improving Relation Extraction with Knowledge-attention](https://mlsanity.com/paper/1910.02724/) (2019)
- ["What's ur type?" Contextualized Classification of User Types in Marijuana-related Communications using Compositional Multiview Embedding](https://mlsanity.com/paper/1806.06813/) (2018)
- [Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate](https://mlsanity.com/paper/1908.06520/) (2019)
- [Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction](https://mlsanity.com/paper/1912.10590/) (2019)
- [Approach for Semi-automatic Construction of Anti-infective Drug Ontology Based on Entity Linking](https://mlsanity.com/paper/1812.01887/) (2018)

## Built on by (most cited first, 7 of 7)

- [NLP is Not enough -- Contextualization of User Input in Chatbots](https://mlsanity.com/paper/2105.06511/) (2021)
- [KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts](https://mlsanity.com/paper/2210.04307/) (2022)
- [Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case](https://mlsanity.com/paper/2304.00025/) (2023)
- [eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarket](https://mlsanity.com/paper/2103.15764/) (2021)
- [Knowledge-Infused Self Attention Transformers](https://mlsanity.com/paper/2306.13501/) (2023)
- [IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations](https://mlsanity.com/paper/2306.13865/) (2023)
- ["Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb](https://mlsanity.com/paper/2304.10512/) (2023)

## 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.