@inproceedings{lymperopoulos-etal-2020-concept,
title = "Concept Wikification for {COVID}-19",
author = "Lymperopoulos, Panagiotis and
Qiu, Haoling and
Min, Bonan",
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.nlpcovid19-2.29",
doi = "10.18653/v1/2020.nlpcovid19-2.29",
abstract = "Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine. Given the very large and ever-growing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles. In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles. We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links. We also develop an end-to-end system for concept wikification for COVID-19. Preliminary experiments show very encouraging results. Our dataset, code and pre-trained model are available at github.com/panlybero/Covid19{\_}wikification.",
}
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<abstract>Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine. Given the very large and ever-growing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles. In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles. We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links. We also develop an end-to-end system for concept wikification for COVID-19. Preliminary experiments show very encouraging results. Our dataset, code and pre-trained model are available at github.com/panlybero/Covid19_wikification.</abstract>
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%0 Conference Proceedings
%T Concept Wikification for COVID-19
%A Lymperopoulos, Panagiotis
%A Qiu, Haoling
%A Min, Bonan
%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 lymperopoulos-etal-2020-concept
%X Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine. Given the very large and ever-growing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles. In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles. We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links. We also develop an end-to-end system for concept wikification for COVID-19. Preliminary experiments show very encouraging results. Our dataset, code and pre-trained model are available at github.com/panlybero/Covid19_wikification.
%R 10.18653/v1/2020.nlpcovid19-2.29
%U https://aclanthology.org/2020.nlpcovid19-2.29
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.29
Markdown (Informal)
[Concept Wikification for COVID-19](https://aclanthology.org/2020.nlpcovid19-2.29) (Lymperopoulos et al., NLP-COVID19 2020)
ACL
- Panagiotis Lymperopoulos, Haoling Qiu, and Bonan Min. 2020. Concept Wikification for COVID-19. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.