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SMM4H at NAACL 2021 2021 : CFP ProfNER shared task: Identification of professions & occupations in Health-related Social Media (SMM4H at NAACL)


When Jun 10, 2021 - Jun 10, 2021
Where Mexico City, Mexico
Abstract Registration Due Mar 1, 2021
Submission Deadline Mar 15, 2021
Notification Due Apr 1, 2021
Final Version Due Apr 12, 2021
Categories    NLP   named entity recognition   social media   AI

Call For Papers

ProfNER Shared Task (SMM4H - NAACL 2021) ***

ProfNER: Identification of professions & occupations in Health-related Social Media (SMM4H at NAACL)

We are organizing the first shared task specifically focusing on named entity recognition of professions & occupations in Social Media in Spanish. Specifically, we focus on Twitter data related to Covid-19 and lock-downs.

ProfNER is part of The Social Media Mining for Health Applications (#SMM4H) Shared Task 2021.

The ProfNER sub-tracks:

Tweet binary classification: Participants must determine whether a tweet contains a mention of occupation, or not.

NER offset detection and classification: Participants must find the beginning and end of occupation mentions and classify them in the corresponding category

Key information:

ProfNER web:



Task motivation

Some workers are at the forefront of the battle against the COVID-19 pandemic. Detecting vulnerable occupations is critical to prepare preventive measures related to exposure to the virus as well as indirect mental health issues due to fear of infection, confinement, etc.

NLP systems benefit from recent NLP technologies such as transformers, novel language technologies and transfer learning and from the vast production of real-time data in social media.

Following the previous organization of shared task with high impact with a considerable number of participants [Cantemist], [CodiEsp], [Meddocan] we are organizing the ProfNER track. It promotes the development of profession & occupation-related text mining resources in Spanish social media due to the special relevance of professions in the definition of at-risk groups.

Systems capable of automatically processing social media texts are of interest to the medical user community, researchers, the pharmaceutical industry as well as patients. The detection of profession & occupation information is relevant for general NLP, occupational data mining, etc.

Competing systems have the potential to generalize to alike use cases in other content types such as medical reports and in other languages.

Important dates

Dec, 15: Training & Development set release
Feb, 15: Validation set submission due [Required]
Mar, 1: Test set & background set release
Mar, 4: Test set predictions due
Mar, 15: System descriptions due
Apr, 1: Acceptance notification
Apr, 12: Camera-ready system descriptions
June 6–11: NAACL 2021 conference

Publications and workshop

Each participating team will have the opportunity to submit a system description which will be published as part of the shared task proceedings.
The 6th SMM4H Workshop, co-located at NAACL 2021 More details are available at

Track Organizers

Martin Krallinger, Barcelona Supercomputing Center, Spain
Antonio Miranda-Escalada, Barcelona Supercomputing Center, Spain
Eulàlia Farré, Barcelona Supercomputing Center, Spain
Salvador Lima, Barcelona Supercomputing Center, Spain

SMM4H Organizers

Graciela Gonzalez-Hernandez, University of Pennsylvania, USA
Davy Weissenbacher, University of Pennsylvania, USA
Ari Z. Klein, University of Pennsylvania, USA
Karen O’Connor, University of Pennsylvania, USA
Abeed Sarker, Emory University, USA
Elena Tutubalina, Kazan Federal University, Russia
Zulfat Miftahutdinov, Kazan Federal University, Russia
Ilsear Alimova, Kazan Federal University, Russia
Martin Krallinger, Barcelona Supercomputing Center, Spain
Juan Banda, Georgia State University, USA

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