@inproceedings{mundotiya-etal-2020-nlprl,
title = "{NLPRL} at {WNUT}-2020 Task 2: {ELM}o-based System for Identification of {COVID}-19 Tweets",
author = "Mundotiya, Rajesh Kumar and
Baruah, Rupjyoti and
Srivastava, Bhavana and
Singh, Anil Kumar",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wnut-1.60",
doi = "10.18653/v1/2020.wnut-1.60",
pages = "419--422",
abstract = "The Coronavirus pandemic has been a dominating news on social media for the last many months. Efforts are being made to reduce its spread and reduce the casualties as well as new infections. For this purpose, the information about the infected people and their related symptoms, as available on social media, such as Twitter, can help in prevention and taking precautions. This is an example of using noisy text processing for disaster management. This paper discusses the NLPRL results in Shared Task-2 of WNUT-2020 workshop. We have considered this problem as a binary classification problem and have used a pre-trained ELMo embedding with GRU units. This approach helps classify the tweets with accuracy as 80.85{\%} and 78.54{\%} as F1-score on the provided test dataset. The experimental code is available online.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mundotiya-etal-2020-nlprl">
<titleInfo>
<title>NLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 Tweets</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rajesh</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Mundotiya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rupjyoti</namePart>
<namePart type="family">Baruah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bhavana</namePart>
<namePart type="family">Srivastava</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anil</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Singh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wei</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alan</namePart>
<namePart type="family">Ritter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tim</namePart>
<namePart type="family">Baldwin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Afshin</namePart>
<namePart type="family">Rahimi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The Coronavirus pandemic has been a dominating news on social media for the last many months. Efforts are being made to reduce its spread and reduce the casualties as well as new infections. For this purpose, the information about the infected people and their related symptoms, as available on social media, such as Twitter, can help in prevention and taking precautions. This is an example of using noisy text processing for disaster management. This paper discusses the NLPRL results in Shared Task-2 of WNUT-2020 workshop. We have considered this problem as a binary classification problem and have used a pre-trained ELMo embedding with GRU units. This approach helps classify the tweets with accuracy as 80.85% and 78.54% as F1-score on the provided test dataset. The experimental code is available online.</abstract>
<identifier type="citekey">mundotiya-etal-2020-nlprl</identifier>
<identifier type="doi">10.18653/v1/2020.wnut-1.60</identifier>
<location>
<url>https://aclanthology.org/2020.wnut-1.60</url>
</location>
<part>
<date>2020-11</date>
<extent unit="page">
<start>419</start>
<end>422</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 Tweets
%A Mundotiya, Rajesh Kumar
%A Baruah, Rupjyoti
%A Srivastava, Bhavana
%A Singh, Anil Kumar
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F mundotiya-etal-2020-nlprl
%X The Coronavirus pandemic has been a dominating news on social media for the last many months. Efforts are being made to reduce its spread and reduce the casualties as well as new infections. For this purpose, the information about the infected people and their related symptoms, as available on social media, such as Twitter, can help in prevention and taking precautions. This is an example of using noisy text processing for disaster management. This paper discusses the NLPRL results in Shared Task-2 of WNUT-2020 workshop. We have considered this problem as a binary classification problem and have used a pre-trained ELMo embedding with GRU units. This approach helps classify the tweets with accuracy as 80.85% and 78.54% as F1-score on the provided test dataset. The experimental code is available online.
%R 10.18653/v1/2020.wnut-1.60
%U https://aclanthology.org/2020.wnut-1.60
%U https://doi.org/10.18653/v1/2020.wnut-1.60
%P 419-422
Markdown (Informal)
[NLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 Tweets](https://aclanthology.org/2020.wnut-1.60) (Mundotiya et al., WNUT 2020)
ACL