@inproceedings{piad-morffis-etal-2020-knowledge,
title = "Knowledge Discovery in {COVID}-19 Research Literature",
author = "Piad-Morffis, Alejandro and
Estevez-Velarde, Suilan and
Estevanell-Valladares, Ernesto Luis and
Guti{\'e}rrez, Yoan and
Montoyo, Andr{\'e}s and
Mu{\~n}oz, Rafael and
Almeida-Cruz, Yudivi{\'a}n",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.22",
doi = "10.18653/v1/2020.nlpcovid19-2.22",
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 by the researchers 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 Piad-Morffis, Alejandro
%A Estevez-Velarde, Suilan
%A Estevanell-Valladares, Ernesto Luis
%A Gutiérrez, Yoan
%A Montoyo, Andrés
%A Muñoz, Rafael
%A Almeida-Cruz, Yudivián
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F piad-morffis-etal-2020-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 by the researchers 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.
%R 10.18653/v1/2020.nlpcovid19-2.22
%U https://aclanthology.org/2020.nlpcovid19-2.22
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.22
Markdown (Informal)
[Knowledge Discovery in COVID-19 Research Literature](https://aclanthology.org/2020.nlpcovid19-2.22) (Piad-Morffis et al., NLP-COVID19 2020)
ACL
- Alejandro Piad-Morffis, Suilan Estevez-Velarde, Ernesto Luis Estevanell-Valladares, Yoan Gutiérrez, Andrés Montoyo, Rafael Muñoz, and Yudivián Almeida-Cruz. 2020. Knowledge Discovery in COVID-19 Research Literature. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.