Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media
2021-06-01NAACL (SMM4H) 2021Unverified0· sign in to hype
Vasile Pais, Maria Mitrofan
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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.