Summary:
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 processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding...
Link:
https://pubmed.ncbi.nlm.nih.gov/36734300/?utm_source=Other&utm_medium=rss&utm_campaign=journals&utm_content=101517697&fc=None&ff=20230215001918&v=2.17.9.post6+86293ac
From feeds:
📚BioDBS Bibliography »
Database (Oxford)
Tags:
Authors:
Davy Weissenbacher, Karen O'Connor, Siddharth Rawal, Yu Zhang, Richard Tzong-Han Tsai, Timothy Miller, Dongfang Xu, Carol Anderson, Bo Liu, Qing Han, Jinfeng Zhang, Igor Kulev, Berkay Köprü, Raul Rodriguez-Esteban, Elif Ozkirimli, Ammer Ayach, Roland Roller, Stephen Piccolo, Peijin Han, V G Vinod Vydiswaran, Ramya Tekumalla, Juan M Banda, Parsa Bagherzadeh, Sabine Bergler, João F Silva, Tiago Almeida, Paloma Martinez, Renzo Rivera-Zavala, Chen-Kai Wang, Hong-Jie Dai, Luis Alberto Robles Hernandez, Graciela Gonzalez-Hernandez
Date tagged:
02/15/2023, 00:19
Date published:
02/03/2023, 06:00