Abstract
This paper describes the entry of the research group SINAI at SMM4H's ProfNER task on identifying professions and occupations in social media data related to health. Specifically, we participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not and also in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them as either professions or working statuses.
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CITATION STYLE
Murgado, J. A. M., Parras Portillo, A. B., López-Úbeda, P., Martín-Valdivia, M. T., & Ureña López, L. A. (2021). Identifying professions & occupations in Health-related Social Media using Natural Language Processing. In Social Media Mining for Health, SMM4H 2021 - Proceedings of the 6th Workshop and Shared Tasks (pp. 141–145). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.smm4h-1.31
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