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This paper summarizes the CLaC submission for SMM4H 2022 Task 10 whichconcerns the recognition of diseases mentioned in Spanish tweets. Beforeclassifying each token, we encode each token with a transformer encoder usingfeatures from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMISTgazetteer, among others. We obtain a strict F1 score of 0.869, with competitionmean of 0.675, standard deviation of 0.245, and median of 0.761.

CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets