Identification of profession & occupation in Health-related Social Media using tweets in Spanish
Victoria Pachón, Jacinto Mata Vázquez, Juan Luís Domínguez Olmedo
Abstract
In this paper we present our approach and system description on Task 7a in ProfNer-ST: Identification of profession & occupation in Health related Social Media. Our main contribution is to show the effectiveness of using BETO-Spanish BERT as a model based on transformers pretrained with a Spanish Corpus for classification tasks. In our experiments we compared several architectures based on transformers with others based on classical machine learning algorithms. With this approach, we achieved an F1-score of 0.92 in the evaluation process.- Anthology ID:
- 2021.smm4h-1.20
- Volume:
- Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 105–107
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.20
- DOI:
- 10.18653/v1/2021.smm4h-1.20
- Cite (ACL):
- Victoria Pachón, Jacinto Mata Vázquez, and Juan Luís Domínguez Olmedo. 2021. Identification of profession & occupation in Health-related Social Media using tweets in Spanish. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 105–107, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Identification of profession & occupation in Health-related Social Media using tweets in Spanish (Pachón et al., SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.smm4h-1.20.pdf