LITL at SMM4H: an old-school feature-based classifier for identifying adverse effects in Tweets

Autor: Tanguy, Ludovic, Ho-Dac, Lydia-Mai, Fabre, Cécile, Bois, Roxane, Haddad, Touati Mohamed Yacine, Ibarboure, Claire, Joyau, Marie, Le Moal, François, Moillic, Jade, Roudaut, Laura, Simounet, Mathilde, Stankovic, Irena, Vanderwaetere, Mickaela

Publikováno v: Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
Graciela Gonzalez-Hernandez; Ari Z. Klein; Davy Weissenbacher; Arjun Magge; Karen O'Connor; Abeed Sarker; Anne-Lyse Minard,; Elena Tutubalina; Zulfat Miftahutdinov; Ilseyar Alimova; Ivan Flores. Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, Association for Computational Linguistics, pp.134-137, 2020

International audience; This paper describes our participation to the SMM4H shared task 2. We designed a linear classifier that estimates whether a tweet mentions an adverse effect associated to a medication. Our system addresses English and French,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::81222624fef865956b86ed06e89bd5af
https://hal.archives-ouvertes.fr/hal-03560385/file/2020.smm4h-1.24.pdf