NLP@NISER: Classification of COVID19 tweets containing symptoms

Deepak Kumar; Nalin Kumar; Subhankar Mishra

doi:10.18653/v1/2021.smm4h-1.19

NLP@NISER: Classification of COVID19 tweets containing symptoms

Deepak Kumar, Nalin Kumar, Subhankar Mishra

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use <b>...</b> for bold, <i>...</i> for italic, and <url>...</url> for URLs.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

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.

Anthology ID:: 2021.smm4h-1.19
Volume:: Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
Month:: June
Year:: 2021
Address:: Mexico City, Mexico
Editors:: Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre-Maduell, Salvador Lima Lopez, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
Venue:: SMM4H
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 102–104
Language:
URL:: https://aclanthology.org/2021.smm4h-1.19/
DOI:: 10.18653/v1/2021.smm4h-1.19
Bibkey:
Cite (ACL):: Deepak Kumar, Nalin Kumar, and Subhankar Mishra. 2021. NLP@NISER: Classification of COVID19 tweets containing symptoms. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 102–104, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):: NLP@NISER: Classification of COVID19 tweets containing symptoms (Kumar et al., SMM4H 2021)
Copy Citation:
PDF:: https://aclanthology.org/2021.smm4h-1.19.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{kumar-etal-2021-nlp,
    title = "{NLP}@{NISER}: Classification of {COVID}19 tweets containing symptoms",
    author = "Kumar, Deepak  and
      Kumar, Nalin  and
      Mishra, Subhankar",
    editor = "Magge, Arjun  and
      Klein, Ari  and
      Miranda-Escalada, Antonio  and
      Al-garadi, Mohammed Ali  and
      Alimova, Ilseyar  and
      Miftahutdinov, Zulfat  and
      Farre-Maduell, Eulalia  and
      Lopez, Salvador Lima  and
      Flores, Ivan  and
      O'Connor, Karen  and
      Weissenbacher, Davy  and
      Tutubalina, Elena  and
      Sarker, Abeed  and
      Banda, Juan M  and
      Krallinger, Martin  and
      Gonzalez-Hernandez, Graciela",
    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."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kumar-etal-2021-nlp">
    <titleInfo>
        <title>NLP@NISER: Classification of COVID19 tweets containing symptoms</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Deepak</namePart>
        <namePart type="family">Kumar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nalin</namePart>
        <namePart type="family">Kumar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Subhankar</namePart>
        <namePart type="family">Mishra</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-06</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>
        <name type="personal">
            <namePart type="given">Arjun</namePart>
            <namePart type="family">Magge</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ari</namePart>
            <namePart type="family">Klein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Miranda-Escalada</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohammed</namePart>
            <namePart type="given">Ali</namePart>
            <namePart type="family">Al-garadi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ilseyar</namePart>
            <namePart type="family">Alimova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zulfat</namePart>
            <namePart type="family">Miftahutdinov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Eulalia</namePart>
            <namePart type="family">Farre-Maduell</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Salvador</namePart>
            <namePart type="given">Lima</namePart>
            <namePart type="family">Lopez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="family">Flores</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Karen</namePart>
            <namePart type="family">O’Connor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Davy</namePart>
            <namePart type="family">Weissenbacher</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Elena</namePart>
            <namePart type="family">Tutubalina</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Abeed</namePart>
            <namePart type="family">Sarker</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Banda</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Martin</namePart>
            <namePart type="family">Krallinger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Graciela</namePart>
            <namePart type="family">Gonzalez-Hernandez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <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>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 &amp; 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>
    <identifier type="citekey">kumar-etal-2021-nlp</identifier>
    <identifier type="doi">10.18653/v1/2021.smm4h-1.19</identifier>
    <location>
        <url>https://aclanthology.org/2021.smm4h-1.19/</url>
    </location>
    <part>
        <date>2021-06</date>
        <extent unit="page">
            <start>102</start>
            <end>104</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T NLP@NISER: Classification of COVID19 tweets containing symptoms
%A Kumar, Deepak
%A Kumar, Nalin
%A Mishra, Subhankar
%Y Magge, Arjun
%Y Klein, Ari
%Y Miranda-Escalada, Antonio
%Y Al-garadi, Mohammed Ali
%Y Alimova, Ilseyar
%Y Miftahutdinov, Zulfat
%Y Farre-Maduell, Eulalia
%Y Lopez, Salvador Lima
%Y Flores, Ivan
%Y O’Connor, Karen
%Y Weissenbacher, Davy
%Y Tutubalina, Elena
%Y Sarker, Abeed
%Y Banda, Juan M.
%Y Krallinger, Martin
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 June
%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

Download as File

Markdown (Informal)

[NLP@NISER: Classification of COVID19 tweets containing symptoms](https://aclanthology.org/2021.smm4h-1.19/) (Kumar et al., SMM4H 2021)

NLP@NISER: Classification of COVID19 tweets containing symptoms (Kumar et al., SMM4H 2021)

ACL

Deepak Kumar, Nalin Kumar, and Subhankar Mishra. 2021. NLP@NISER: Classification of COVID19 tweets containing symptoms. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 102–104, Mexico City, Mexico. Association for Computational Linguistics.