NLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 Tweets

Rajesh Kumar Mundotiya, Rupjyoti Baruah, Bhavana Srivastava, Anil Kumar Singh

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.
Anthology ID:
2020.wnut-1.60
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
419–422
Language:
URL:
https://aclanthology.org/2020.wnut-1.60
DOI:
10.18653/v1/2020.wnut-1.60
Bibkey:
Cite (ACL):
Rajesh Kumar Mundotiya, Rupjyoti Baruah, Bhavana Srivastava, and Anil Kumar Singh. 2020. NLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 419–422, Online. Association for Computational Linguistics.
Cite (Informal):
NLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 Tweets (Mundotiya et al., WNUT 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-2/2020.wnut-1.60.pdf
Data
WNUT-2020 Task 2
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