@inproceedings{afonso-etal-2024-bit,
title = "{BIT}@{UA} at {\#}{SMM}4{H} 2024 Tasks 1 and 5: finding adverse drug events and children{'}s medical disorders in {E}nglish tweets",
author = "Afonso, Luis and
Almeida, Jo{\~a}o and
Antunes, Rui and
Oliveira, Jos{\'e}",
editor = "Xu, Dongfang and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.smm4h-1.37",
pages = "158--162",
abstract = "In this paper we present our proposed systems, for Tasks 1 and 5 of the {\#}SMM4H-2024 shared task (Social Media Mining for Health), responsible for identifying health-related aspects in English social media text. Task 1 consisted of identifying text spans mentioning adverse drug events and linking them to unique identifiers from the medical terminology MedDRA, whereas in Task 5 the aim was to distinguish tweets that report a user having a child with a medical disorder from tweets that merely mention a disorder.For Task 1, our system, composed of a pre-trained RoBERTa model and a random forest classifier, achieved 0.397 and 0.295 entity recognition and normalization F1-scores respectively. In Task 5, we obtained a 0.840 F1-score using a pre-trained BERT model.",
}
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<abstract>In this paper we present our proposed systems, for Tasks 1 and 5 of the #SMM4H-2024 shared task (Social Media Mining for Health), responsible for identifying health-related aspects in English social media text. Task 1 consisted of identifying text spans mentioning adverse drug events and linking them to unique identifiers from the medical terminology MedDRA, whereas in Task 5 the aim was to distinguish tweets that report a user having a child with a medical disorder from tweets that merely mention a disorder.For Task 1, our system, composed of a pre-trained RoBERTa model and a random forest classifier, achieved 0.397 and 0.295 entity recognition and normalization F1-scores respectively. In Task 5, we obtained a 0.840 F1-score using a pre-trained BERT model.</abstract>
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%0 Conference Proceedings
%T BIT@UA at #SMM4H 2024 Tasks 1 and 5: finding adverse drug events and children’s medical disorders in English tweets
%A Afonso, Luis
%A Almeida, João
%A Antunes, Rui
%A Oliveira, José
%Y Xu, Dongfang
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F afonso-etal-2024-bit
%X In this paper we present our proposed systems, for Tasks 1 and 5 of the #SMM4H-2024 shared task (Social Media Mining for Health), responsible for identifying health-related aspects in English social media text. Task 1 consisted of identifying text spans mentioning adverse drug events and linking them to unique identifiers from the medical terminology MedDRA, whereas in Task 5 the aim was to distinguish tweets that report a user having a child with a medical disorder from tweets that merely mention a disorder.For Task 1, our system, composed of a pre-trained RoBERTa model and a random forest classifier, achieved 0.397 and 0.295 entity recognition and normalization F1-scores respectively. In Task 5, we obtained a 0.840 F1-score using a pre-trained BERT model.
%U https://aclanthology.org/2024.smm4h-1.37
%P 158-162
Markdown (Informal)
[BIT@UA at #SMM4H 2024 Tasks 1 and 5: finding adverse drug events and children’s medical disorders in English tweets](https://aclanthology.org/2024.smm4h-1.37) (Afonso et al., SMM4H-WS 2024)
ACL