@inproceedings{valdes-etal-2021-uach,
title = "{UACH}-{INAOE} at {SMM}4{H}: a {BERT} based approach for classification of {COVID}-19 {T}witter posts",
author = "Valdes, Alberto and
Lopez, Jesus and
Montes, Manuel",
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-1.10",
doi = "10.18653/v1/2021.smm4h-1.10",
pages = "65--68",
abstract = "This work describes the participation of the Universidad Aut{\'o}noma de Chihuahua - Instituto Nacional de Astrof{\'\i}sica, {\'O}ptica y Electr{\'o}nica team at the Social Media Mining for Health Applications (SMM4H) 2021 shared task. Our team participated in task 5 and 6, both focused on the automatic classification of Twitter posts related to COVID-19. Task 5 was oriented on solving a binary classification problem, trying to identify self-reporting tweets of potential cases of COVID-19. Task 6 objective was to classify tweets containing COVID-19 symptoms. For both tasks we used models based on bidirectional encoder representations from transformers (BERT). Our objective was to determine if a model pretrained on a corpus in the domain of interest can outperform one trained on a much larger general domain corpus. Our F1 results were encouraging, 0.77 and 0.95 for task 5 and 6 respectively, having achieved the highest score among all the participants in the latter.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="valdes-etal-2021-uach">
<titleInfo>
<title>UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alberto</namePart>
<namePart type="family">Valdes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jesus</namePart>
<namePart type="family">Lopez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manuel</namePart>
<namePart type="family">Montes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-jun</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Mexico City, Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This work describes the participation of the Universidad Autónoma de Chihuahua - Instituto Nacional de Astrofísica, Óptica y Electrónica team at the Social Media Mining for Health Applications (SMM4H) 2021 shared task. Our team participated in task 5 and 6, both focused on the automatic classification of Twitter posts related to COVID-19. Task 5 was oriented on solving a binary classification problem, trying to identify self-reporting tweets of potential cases of COVID-19. Task 6 objective was to classify tweets containing COVID-19 symptoms. For both tasks we used models based on bidirectional encoder representations from transformers (BERT). Our objective was to determine if a model pretrained on a corpus in the domain of interest can outperform one trained on a much larger general domain corpus. Our F1 results were encouraging, 0.77 and 0.95 for task 5 and 6 respectively, having achieved the highest score among all the participants in the latter.</abstract>
<identifier type="citekey">valdes-etal-2021-uach</identifier>
<identifier type="doi">10.18653/v1/2021.smm4h-1.10</identifier>
<location>
<url>https://aclanthology.org/2021.smm4h-1.10</url>
</location>
<part>
<date>2021-jun</date>
<extent unit="page">
<start>65</start>
<end>68</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts
%A Valdes, Alberto
%A Lopez, Jesus
%A Montes, Manuel
%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 valdes-etal-2021-uach
%X This work describes the participation of the Universidad Autónoma de Chihuahua - Instituto Nacional de Astrofísica, Óptica y Electrónica team at the Social Media Mining for Health Applications (SMM4H) 2021 shared task. Our team participated in task 5 and 6, both focused on the automatic classification of Twitter posts related to COVID-19. Task 5 was oriented on solving a binary classification problem, trying to identify self-reporting tweets of potential cases of COVID-19. Task 6 objective was to classify tweets containing COVID-19 symptoms. For both tasks we used models based on bidirectional encoder representations from transformers (BERT). Our objective was to determine if a model pretrained on a corpus in the domain of interest can outperform one trained on a much larger general domain corpus. Our F1 results were encouraging, 0.77 and 0.95 for task 5 and 6 respectively, having achieved the highest score among all the participants in the latter.
%R 10.18653/v1/2021.smm4h-1.10
%U https://aclanthology.org/2021.smm4h-1.10
%U https://doi.org/10.18653/v1/2021.smm4h-1.10
%P 65-68
Markdown (Informal)
[UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts](https://aclanthology.org/2021.smm4h-1.10) (Valdes et al., SMM4H 2021)
ACL