Mauricio Araneda-Hernandez


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PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features
Matias Rojas | Jose Barros | Kinan Martin | Mauricio Araneda-Hernandez | Jocelyn Dunstan
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

This paper describes our approaches used to solve the SocialDisNER task, which belongs to the Social Media Mining for Health Applications (SMM4H) shared task. This task aims to identify disease mentions in tweets written in Spanish. The proposed model is an architecture based on the FLERT approach. It consists of fine-tuning a language model that creates an input representation of a sentence based on its neighboring sentences, thus obtaining the document-level context. The best result was obtained using an ensemble of six language models using the FLERT approach. The system achieved an F1 score of 0.862, significantly surpassing the average performance among competitor models of 0.680 on the test partition.