Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media

Vasile Pais, Maria Mitrofan


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
Venues:
NAACL | 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
Bibkey:
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)
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PDF:
https://aclanthology.org/2021.smm4h-1.27.pdf