@inproceedings{lichouri-abbas-2020-speechtrans,
title = "{S}peech{T}rans@{SMM}4{H}{'}20: Impact of Preprocessing and N-grams on Automatic Classification of Tweets That Mention Medications",
author = "Lichouri, Mohamed and
Abbas, Mourad",
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-1.19",
pages = "118--120",
abstract = "This paper describes our system developed for automatically classifying tweets that mention medications. We used the Decision Tree classifier for this task. We have shown that using some elementary preprocessing steps and TF-IDF n-grams led to acceptable classifier performance. Indeed, the F1-score recorded was 74.58{\%} in the development phase and 63.70{\%} in the test phase.",
}
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%0 Conference Proceedings
%T SpeechTrans@SMM4H’20: Impact of Preprocessing and N-grams on Automatic Classification of Tweets That Mention Medications
%A Lichouri, Mohamed
%A Abbas, Mourad
%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 lichouri-abbas-2020-speechtrans
%X This paper describes our system developed for automatically classifying tweets that mention medications. We used the Decision Tree classifier for this task. We have shown that using some elementary preprocessing steps and TF-IDF n-grams led to acceptable classifier performance. Indeed, the F1-score recorded was 74.58% in the development phase and 63.70% in the test phase.
%U https://aclanthology.org/2020.smm4h-1.19
%P 118-120
Markdown (Informal)
[SpeechTrans@SMM4H’20: Impact of Preprocessing and N-grams on Automatic Classification of Tweets That Mention Medications](https://aclanthology.org/2020.smm4h-1.19) (Lichouri & Abbas, SMM4H 2020)
ACL