Identifying professions & occupations in Health-related Social Media using Natural Language Processing
2021-06-01NAACL (SMM4H) 2021Unverified0· sign in to hype
Alberto Mesa Murgado, Ana Parras Portillo, Pilar López Úbeda, Maite Martin, Alfonso Ureña-López
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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.