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Chapitre D'ouvrage Année : 2020

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

Résumé

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, and is based on a number of ad-hoc word lists and features. These cues were mostly obtained through an extensive corpus analysis of the provided training data. Different weighting schemes were tested (manually tuned or based on a logistic regression), the best one achieving a F1 score of 0.31 for English and 0.15 for French.

Domaines

Linguistique
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Dates et versions

hal-03560385 , version 1 (18-02-2022)

Identifiants

  • HAL Id : hal-03560385 , version 1

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Ludovic Tanguy, Lydia-Mai Ho-Dac, Cécile Fabre, Roxane Bois, Touati Mohamed Yacine Haddad, et al.. LITL at SMM4H: an old-school feature-based classifier for identifying adverse effects in Tweets. 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. ⟨hal-03560385⟩
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