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With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators.
Persistent depression and anxiety in the united states: prevalence and quality of care,
A. S. Young, R. Klap, R. Shoai, K. B. Wells, · 2008
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Exploiting background knowledge for relation extraction,
Y. S. Chan, D. Roth, · 2010
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Promoting innovation and excellence to face the rapid diffusion of novel psychoactive substances in the EU: the outcomes of the ReDNet project,
O. Corazza, S. Assi, P. Simonato, J. Corkery, F. S. Bersani, Z. Demetrovics, J. Stair, S. Fergus, C. Pezzolesi, M. Pasinetti, P. Deluca, C. Drummond, Z. Davey, U. Blaszko, J. Moskalewicz, B. Mervo, L. D. Furia, M. Farre, L. Flesland, A. Pisarska, H. Shapiro, H. Siemann, A. Skutle, E. Sferrazza, M. Torrens, F. Sambola, P. van der Kreeft, N. Scherbaum, F. Schifano, · 2013
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PREDOSE: a semantic web platform for drug abuse epidemiology using social media,
D. Cameron, G. A. Smith, R. Daniulaityte, A. P. Sheth, D. Dave, L. Chen, G. Anand, R. Carlson, K. Z. Watkins, R. Falck, · 2013
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Predose: a semantic web platform for drug abuse epidemiology using social media,
D. Cameron, G. A. Smith, R. Daniulaityte, A. P. Sheth, D. Dave, L. Chen, G. Anand, R. Carlson, K. Z. Watkins, R. Falck, · 2013
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Convolution neural network for relation extraction,
C. Liu, W. Sun, W. Chao, W. Che, · 2013
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State medical marijuana laws,
K. Hanson, A. Garcia, · 2014
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R. Room, Legalizing a market for cannabis for pleasure: Colorado, washington, uruguay and beyond, 2014
2014
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Adverse health effects of marijuana use,
N. D. Volkow, R. D. Baler, W. M. Compton, S. R. B. Weiss, · 2014
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Monitoring drug markets in the internet age and the evolution of drug monitoring systems in australia,
L. Burns, A. Roxburgh, R. Bruno, J. Van Buskirk, · 2014
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Deep metric learning using triplet network,
E. Hoffer, N. Ailon, · 2015
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How does state marijuana policy affect US youth? medical marijuana laws, marijuana use and perceived harmfulness: 1991-2014,
K. M. Keyes, M. Wall, M. Cerdá, J. Schulenberg, P. M. O’Malley, S. Galea, T. Feng, D. S. Hasin, · 2016
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Neural relation extraction with selective attention over instances,
Y. Lin, S. Shen, Z. Liu, H. Luan, M. Sun, · 2016
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End-to-end relation extraction using lstms on sequences and tree structures,
M. Miwa, M. Bansal, · 2016
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Medicinal cannabis: History, pharmacology, and implications for the acute care setting,
M. B. Bridgeman, D. T. Abazia, · 2017
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“retweet to pass the blunt”: Analyzing geographic and content features of Cannabis-Related tweeting across the united states,
R. Daniulaityte, F. R. Lamy, G. A. Smith, R. W. Nahhas, R. G. Carlson, K. Thirunarayan, S. S. Martins, E. W. Boyer, A. Sheth, · 2017
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Combining word-level and character-level representations for relation classification of informal text,
D. Liang, W. Xu, Y. Zhao, · 2017
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Deep learning for extracting protein-protein interactions from biomedical literature,
Y. Peng, Z. Lu, · 2017
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Health conditions and motivations for marijuana use among young adult medical marijuana patients and non-patient marijuana users,
S. E. Lankenau, J. Ataiants, S. Mohanty, S. Schrager, E. Iverson, C. F. Wong, · 2018
Cited alongside, same era.
“no high like a brownie high”: A content analysis of edible marijuana tweets,
P. A. Cavazos-Rehg, K. Zewdie, M. J. Krauss, S. J. Sowles, · 2018
Cited alongside, same era.
“you got to love rosin: Solventless dabs, pure, clean, natural medicine.” exploring twitter data on emerging trends in rosin tech marijuana concentrates,
F. R. Lamy, R. Daniulaityte, M. Zatreh, R. W. Nahhas, A. Sheth, S. S. Martins, E. W. Boyer, R. G. Carlson, · 2018
Cited alongside, same era.
Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in ohio,
U. Lokala, F. R. Lamy, R. Daniulaityte, A. Sheth, R. W. Nahhas, J. I. Roden, S. Yadav, R. G. Carlson, · 2018
Cited alongside, same era.
What’s ur type? contextualized classification of user types in marijuana-related communications using compositional multiview embedding,
Feature assisted stacked attentive shortest dependency path based bi-lstm model for protein–protein interaction,
S. Yadav, A. Ekbal, S. Saha, A. Kumar, P. Bhattacharyya, · 2019
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Simplifying graph convolutional networks,
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, K. Weinberger, · 2019
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Attention guided graph convolutional networks for relation extraction,
Z. Guo, Y. Zhang, W. Lu, · 2019
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Biobert: pre-trained biomedical language representation model for biomedical text mining,
J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, J. Kang, · 2019
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Scibert: A pretrained language model for scientific text,
I. Beltagy, K. Lo, A. Cohan, · 2019
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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U. Kursuncu, M. Gaur, U. Lokala, A. Illendula, K. Thirunarayan, R. Daniulaityte, A. Sheth, I. B. Arpinar, · 2018
Cited alongside, same era.
Let me tell you about your mental health!: Contextualized classification of reddit posts to DSM-5 for web-based intervention,
M. Gaur, U. Kursuncu, A. Alambo, A. Sheth, R. Daniulaityte, K. Thirunarayan, J. Pathak, · 2018
Cited alongside, same era.
Adversarial training for multi-context joint entity and relation extraction,
G. Bekoulis, J. Deleu, T. Demeester, C. Develder, · 2018
Cited alongside, same era.
Extraction of protein–protein interactions (ppis) from the literature by deep convolutional neural networks with various feature embeddings,
S.-P. Choi, · 2018
Cited alongside, same era.
Attention-Aware Path-Based relation extraction for medical knowledge graph,
D. Wen, Y. Liu, K. Yuan, S. Si, Y. Shen, · 2018
Cited alongside, same era.
Drug abuse ontology — NCBO BioPortal, http://bioportal.bioontology.org/ontologies/DAO , ???? Accessed: 2019-2-28
2019
Cited alongside, same era.
Knowledge-aware assessment of severity of suicide risk for early intervention,
M. Gaur, A. Alambo, J. P. Sain, U. Kursuncu, K. Thirunarayan, R. Kavuluru, A. Sheth, R. Welton, J. Pathak, · 2019
Cited alongside, same era.
Semantic relation classification via bidirectional lstm networks with entity-aware attention using latent entity typing,
J. Lee, S. Seo, Y. S. Choi, · 2019
Cited alongside, same era.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, Q. V. Le, · 2019
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Simple bert models for relation extraction and semantic role labeling,
P. Shi, J. Lin, · 2019
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Fine-tuning bert for joint entity and relation extraction in chinese medical text,
K. Xue, Y. Zhou, Z. Ma, T. Ruan, H. Zhang, P. He, · 2019
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Extracting multiple-relations in one-pass with pre-trained transformers,
H. Wang, M. Tan, M. Yu, S. Chang, D. Wang, K. Xu, X. Guo, S. Potdar, · 2019
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Neural relation extraction for knowledge base enrichment,
B. Distiawan, G. Weikum, J. Qi, R. Zhang, · 2019
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Dual cnn for relation extraction with knowledge-based attention and word embeddings,
J. Li, G. Huang, J. Chen, Y. Wang, · 2019
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Knowledge-guided convolutional networks for chemical-disease relation extraction,
H. Zhou, C. Lang, Z. Liu, S. Ning, Y. Lin, L. Du, · 2019
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Improving relation extraction with knowledge-attention,
P. Li, K. Mao, X. Yang, Q. Li, · 2019
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Question answering for suicide risk assessment using reddit,
A. Alambo, M. Gaur, U. Lokala, U. Kursuncu, K. Thirunarayan, A. Gyrard, A. Sheth, R. S. Welton, J. Pathak, · 2019
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U. Kursuncu, M. Gaur, C. Castillo, A. Alambo, K. Thirunarayan, V. Shalin, D. Achilov, I. B. Arpinar, A. Sheth, · 2019
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Language models are few-shot learners,
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., · 2020
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Knowledge-infused deep learning,
M. Gaur, U. Kursuncu, A. Sheth, R. Wickramarachchi, S. Yadav, · 2020
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M. Gaur, K. Faldu, A. Sheth, · 2020
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