@inproceedings{kumar-etal-2021-nlp,
title = "{NLP}@{NISER}: Classification of {COVID}19 tweets containing symptoms",
author = "Kumar, Deepak and
Kumar, Nalin and
Mishra, Subhankar",
booktitle = "Proceedings of the Sixth Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.smm4h-1.19",
doi = "10.18653/v1/2021.smm4h-1.19",
pages = "102--104",
abstract = "In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports {\&} literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.",
}
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<abstract>In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.</abstract>
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%0 Conference Proceedings
%T NLP@NISER: Classification of COVID19 tweets containing symptoms
%A Kumar, Deepak
%A Kumar, Nalin
%A Mishra, Subhankar
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F kumar-etal-2021-nlp
%X In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.
%R 10.18653/v1/2021.smm4h-1.19
%U https://aclanthology.org/2021.smm4h-1.19
%U https://doi.org/10.18653/v1/2021.smm4h-1.19
%P 102-104
Markdown (Informal)
[NLP@NISER: Classification of COVID19 tweets containing symptoms](https://aclanthology.org/2021.smm4h-1.19) (Kumar et al., SMM4H 2021)
ACL