Identifying professions & occupations in Health-related Social Media using Natural Language Processing
Alberto Mesa Murgado, Ana Parras Portillo, Pilar López Úbeda, Maite Martin, Alfonso Ureña-López
Abstract
This paper describes the entry of the research group SINAI at SMM4H’s ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not, as well as in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them discriminating by professions and working statuses.- Anthology ID:
- 2021.smm4h-1.31
- Volume:
- Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Editors:
- Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre-Maduell, Salvador Lima Lopez, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 141–145
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.31
- DOI:
- 10.18653/v1/2021.smm4h-1.31
- Cite (ACL):
- Alberto Mesa Murgado, Ana Parras Portillo, Pilar López Úbeda, Maite Martin, and Alfonso Ureña-López. 2021. Identifying professions & occupations in Health-related Social Media using Natural Language Processing. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 141–145, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Identifying professions & occupations in Health-related Social Media using Natural Language Processing (Mesa Murgado et al., SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.smm4h-1.31.pdf
- Data
- SMM4H