@inproceedings{tanguy-etal-2020-litl,
title = "{LITL} at {SMM}4{H}: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets",
author = "Tanguy, Ludovic and
Ho-Dac, Lydia-Mai and
Fabre, C{\'e}cile and
Bois, Roxane and
Haddad, Touati Mohamed Yacine and
Ibarboure, Claire and
Joyau, Marie and
Le moal, Fran{\c{c}}ois and
Moiilic, Jade and
Roudaut, Laura and
Simounet, Mathilde and
Stankovic, Irena and
Vandewaetere, Mickaela",
editor = "Gonzalez-Hernandez, Graciela and
Klein, Ari Z. and
Flores, Ivan and
Weissenbacher, Davy and
Magge, Arjun and
O'Connor, Karen and
Sarker, Abeed and
Minard, Anne-Lyse and
Tutubalina, Elena and
Miftahutdinov, Zulfat and
Alimova, Ilseyar",
booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.smm4h-1.24/",
pages = "134--137",
abstract = "This paper describes our participation to the SMM4H shared task 2. We designed a rule-based 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 specific 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."
}
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<abstract>This paper describes our participation to the SMM4H shared task 2. We designed a rule-based 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 specific 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.</abstract>
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%0 Conference Proceedings
%T LITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets
%A Tanguy, Ludovic
%A Ho-Dac, Lydia-Mai
%A Fabre, Cécile
%A Bois, Roxane
%A Haddad, Touati Mohamed Yacine
%A Ibarboure, Claire
%A Joyau, Marie
%A Le moal, François
%A Moiilic, Jade
%A Roudaut, Laura
%A Simounet, Mathilde
%A Stankovic, Irena
%A Vandewaetere, Mickaela
%Y Gonzalez-Hernandez, Graciela
%Y Klein, Ari Z.
%Y Flores, Ivan
%Y Weissenbacher, Davy
%Y Magge, Arjun
%Y O’Connor, Karen
%Y Sarker, Abeed
%Y Minard, Anne-Lyse
%Y Tutubalina, Elena
%Y Miftahutdinov, Zulfat
%Y Alimova, Ilseyar
%S Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F tanguy-etal-2020-litl
%X This paper describes our participation to the SMM4H shared task 2. We designed a rule-based 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 specific 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.
%U https://aclanthology.org/2020.smm4h-1.24/
%P 134-137
Markdown (Informal)
[LITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets](https://aclanthology.org/2020.smm4h-1.24/) (Tanguy et al., SMM4H 2020)
ACL
- Ludovic Tanguy, Lydia-Mai Ho-Dac, Cécile Fabre, Roxane Bois, Touati Mohamed Yacine Haddad, Claire Ibarboure, Marie Joyau, François Le moal, Jade Moiilic, Laura Roudaut, Mathilde Simounet, Irena Stankovic, and Mickaela Vandewaetere. 2020. LITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 134–137, Barcelona, Spain (Online). Association for Computational Linguistics.