@inproceedings{avram-etal-2022-racai,
    title = "{RACAI}@{SMM}4{H}{'}22: Tweets Disease Mention Detection Using a Neural Lateral Inhibitory Mechanism",
    author = "Avram, Andrei-Marius  and
      Pais, Vasile  and
      Mitrofan, Maria",
    editor = "Gonzalez-Hernandez, Graciela  and
      Weissenbacher, Davy",
    booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.smm4h-1.1",
    pages = "1--3",
    abstract = "This paper presents our system employed for the Social Media Mining for Health (SMM4H) 2022 competition Task 10 - SocialDisNER. The goal of the task was to improve the detection of diseases in tweets. Because the tweets were in Spanish, we approached this problem using a system that relies on a pre-trained multilingual model and is fine-tuned using the recently introduced lateral inhibition layer. We further experimented on this task by employing a conditional random field on top of the system and using a voting-based ensemble that contains various architectures. The evaluation results outlined that our best performing model obtained 83.7{\%} F1-strict on the validation set and 82.1{\%} F1-strict on the test set.",
}