Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user’s publicly available tweets (the user’s ‘timeline’). In general, detecting health-related tweets is notoriously challenging for natural language...

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Detalles Bibliográficos
Autores principales: Weissenbacher, Davy, O’Connor, Karen, Rawal, Siddharth, Zhang, Yu, Tsai, Richard Tzong-Han, Miller, Timothy, Xu, Dongfang, Anderson, Carol, Liu, Bo, Han, Qing, Zhang, Jinfeng, Kulev, Igor, Köprü, Berkay, Rodriguez-Esteban, Raul, Ozkirimli, Elif, Ayach, Ammer, Roller, Roland, Piccolo, Stephen, Han, Peijin, Vydiswaran, V G Vinod, Tekumalla, Ramya, Banda, Juan M, Bagherzadeh, Parsa, Bergler, Sabine, Silva, João F, Almeida, Tiago, Martinez, Paloma, Rivera-Zavala, Renzo, Wang, Chen-Kai, Dai, Hong-Jie, Alberto Robles Hernandez, Luis, Gonzalez-Hernandez, Graciela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896308/
https://www.ncbi.nlm.nih.gov/pubmed/36734300
http://dx.doi.org/10.1093/database/baac108

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896308/
https://www.ncbi.nlm.nih.gov/pubmed/36734300
http://dx.doi.org/10.1093/database/baac108

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