@inproceedings{estevanell-valladares-etal-2021-knowledge,
title = "Knowledge Discovery in {COVID}-19 Research Literature",
author = "Estevanell-Valladares, Ernesto L. and
Estevez-Velarde, Suilan and
Piad-Morffis, Alejandro and
Gutierrez, Yoan and
Montoyo, Andres and
Mu{\~n}oz, Rafael and
Almeida Cruz, Yudivi{\'a}n",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.46",
pages = "402--410",
abstract = "This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.",
}
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%0 Conference Proceedings
%T Knowledge Discovery in COVID-19 Research Literature
%A Estevanell-Valladares, Ernesto L.
%A Estevez-Velarde, Suilan
%A Piad-Morffis, Alejandro
%A Gutierrez, Yoan
%A Montoyo, Andres
%A Muñoz, Rafael
%A Almeida Cruz, Yudivián
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F estevanell-valladares-etal-2021-knowledge
%X This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.
%U https://aclanthology.org/2021.ranlp-1.46
%P 402-410
Markdown (Informal)
[Knowledge Discovery in COVID-19 Research Literature](https://aclanthology.org/2021.ranlp-1.46) (Estevanell-Valladares et al., RANLP 2021)
ACL
- Ernesto L. Estevanell-Valladares, Suilan Estevez-Velarde, Alejandro Piad-Morffis, Yoan Gutierrez, Andres Montoyo, Rafael Muñoz, and Yudivián Almeida Cruz. 2021. Knowledge Discovery in COVID-19 Research Literature. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 402–410, Held Online. INCOMA Ltd..