Abstract
This paper presents our contribution to the ProfNER shared task. Our work focused on evaluating different pre-trained word embedding representations suitable for the task. We further explored combinations of embeddings in order to improve the overall results.- Anthology ID:
- 2021.smm4h-1.27
- 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:
- 128–130
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.27
- DOI:
- 10.18653/v1/2021.smm4h-1.27
- Cite (ACL):
- Vasile Pais and Maria Mitrofan. 2021. Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 128–130, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media (Pais & Mitrofan, SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.smm4h-1.27.pdf