@inproceedings{dahiya-bagga-2024-cogai,
title = "{C}og{AI}@{SMM}4{H} 2024: Leveraging {BERT}-based Ensemble Models for Classifying Tweets on Developmental Disorders",
author = "Dahiya, Liza and
Bagga, Rachit",
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-1.26",
pages = "114--116",
abstract = "This paper presents our work for the Task 5 of the Social Media Mining for Health Applications 2024 Shared Task - Binary classification of English tweets reporting children{'}s medical disorders. In this paper, we present and compare multiple approaches for automatically classifying tweets from parents based on whether they mention having a child with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma. We use ensemble of various BERT-based models trained on provided dataset that yields an F1 score of \textbf{0.901} on the test data.",
}
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<abstract>This paper presents our work for the Task 5 of the Social Media Mining for Health Applications 2024 Shared Task - Binary classification of English tweets reporting children’s medical disorders. In this paper, we present and compare multiple approaches for automatically classifying tweets from parents based on whether they mention having a child with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma. We use ensemble of various BERT-based models trained on provided dataset that yields an F1 score of 0.901 on the test data.</abstract>
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%0 Conference Proceedings
%T CogAI@SMM4H 2024: Leveraging BERT-based Ensemble Models for Classifying Tweets on Developmental Disorders
%A Dahiya, Liza
%A Bagga, Rachit
%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 dahiya-bagga-2024-cogai
%X This paper presents our work for the Task 5 of the Social Media Mining for Health Applications 2024 Shared Task - Binary classification of English tweets reporting children’s medical disorders. In this paper, we present and compare multiple approaches for automatically classifying tweets from parents based on whether they mention having a child with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma. We use ensemble of various BERT-based models trained on provided dataset that yields an F1 score of 0.901 on the test data.
%U https://aclanthology.org/2024.smm4h-1.26
%P 114-116
Markdown (Informal)
[CogAI@SMM4H 2024: Leveraging BERT-based Ensemble Models for Classifying Tweets on Developmental Disorders](https://aclanthology.org/2024.smm4h-1.26) (Dahiya & Bagga, SMM4H-WS 2024)
ACL