The LasigeBioTM team composed by Pedro Ruas, Vitor D. T. Andrade, and Francisco M. Couto achieved 2nd place at the Subtrack A of the ProfNER shared-task, which occurred in the context of the Social Media Mining for Health Applications (#SMM4H) ’21 Shared Task. Subtrack A was a tweet binary classification task, where the goal was to classify Spanish covid-19-related tweets according to the presence of entity mentions associated with professions, working statutes, and other work-related activities. The system developed by LasigeBioTM achieved an F1-score of 0.92, which was close to the result achieved by the top-performer system (0.93), and achieved the highest precision, 0.95. The complete results are available in the task overview paper.
The paper describing the developed system “Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification” is included in the Proceedings of the Sixth SMM4H Workshop and Shared Tasks, and it is available here.