@inproceedings{guo-etal-2020-emory,
title = "Emory at {WNUT}-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification",
author = "Guo, Yuting and
Ali Al-Garadi, Mohammed and
Sarker, Abeed",
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-1.54",
doi = "10.18653/v1/2020.wnut-1.54",
pages = "388--393",
abstract = "This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: {``}Identifi- cation of Informative COVID-19 English Tweet{''}. Our system explores three recent Transformer- based deep learning models pretrained on large- scale data to encode documents. Moreover, we developed two feature enrichment methods to en- hance document embeddings by integrating emoji embeddings and syntactic features into deep learn- ing models. Our system achieved F1-score of 0.897 and accuracy of 90.1{\%} on the test set, and ranked in the top-third of all 55 teams.",
}
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<abstract>This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: “Identifi- cation of Informative COVID-19 English Tweet”. Our system explores three recent Transformer- based deep learning models pretrained on large- scale data to encode documents. Moreover, we developed two feature enrichment methods to en- hance document embeddings by integrating emoji embeddings and syntactic features into deep learn- ing models. Our system achieved F1-score of 0.897 and accuracy of 90.1% on the test set, and ranked in the top-third of all 55 teams.</abstract>
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%0 Conference Proceedings
%T Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification
%A Guo, Yuting
%A Ali Al-Garadi, Mohammed
%A Sarker, Abeed
%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 guo-etal-2020-emory
%X This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: “Identifi- cation of Informative COVID-19 English Tweet”. Our system explores three recent Transformer- based deep learning models pretrained on large- scale data to encode documents. Moreover, we developed two feature enrichment methods to en- hance document embeddings by integrating emoji embeddings and syntactic features into deep learn- ing models. Our system achieved F1-score of 0.897 and accuracy of 90.1% on the test set, and ranked in the top-third of all 55 teams.
%R 10.18653/v1/2020.wnut-1.54
%U https://aclanthology.org/2020.wnut-1.54
%U https://doi.org/10.18653/v1/2020.wnut-1.54
%P 388-393
Markdown (Informal)
[Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification](https://aclanthology.org/2020.wnut-1.54) (Guo et al., WNUT 2020)
ACL