@inproceedings{reddy-biswal-2020-iiitbh,
title = "{IIITBH} at {WNUT}-2020 Task 2: Exploiting the best of both worlds",
author = "Reddy, Saichethan and
Biswal, Pradeep",
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-1.46",
doi = "10.18653/v1/2020.wnut-1.46",
pages = "342--346",
abstract = "In this paper, we present IIITBH team{'}s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the task is to develop a system that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is Informative or not. Our approach is based on exploiting semantic information from both max pooling and average pooling, to this end we propose two models.",
}
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<abstract>In this paper, we present IIITBH team’s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the task is to develop a system that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is Informative or not. Our approach is based on exploiting semantic information from both max pooling and average pooling, to this end we propose two models.</abstract>
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%0 Conference Proceedings
%T IIITBH at WNUT-2020 Task 2: Exploiting the best of both worlds
%A Reddy, Saichethan
%A Biswal, Pradeep
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F reddy-biswal-2020-iiitbh
%X In this paper, we present IIITBH team’s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the task is to develop a system that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is Informative or not. Our approach is based on exploiting semantic information from both max pooling and average pooling, to this end we propose two models.
%R 10.18653/v1/2020.wnut-1.46
%U https://aclanthology.org/2020.wnut-1.46
%U https://doi.org/10.18653/v1/2020.wnut-1.46
%P 342-346
Markdown (Informal)
[IIITBH at WNUT-2020 Task 2: Exploiting the best of both worlds](https://aclanthology.org/2020.wnut-1.46) (Reddy & Biswal, WNUT 2020)
ACL