@inproceedings{grotheer-etal-2020-covid,
title = "{COVID}-19 Literature Topic-Based Search via Hierarchical {NMF}",
author = "Grotheer, Rachel and
Huang, Longxiu and
Huang, Yihuan and
Kryshchenko, Alona and
Kryshchenko, Oleksandr and
Li, Pengyu and
Li, Xia and
Rebrova, Elizaveta and
Ha, Kyung and
Needell, Deanna",
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.nlpcovid19-2.4",
doi = "10.18653/v1/2020.nlpcovid19-2.4",
abstract = "A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.",
}
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<abstract>A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.</abstract>
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%0 Conference Proceedings
%T COVID-19 Literature Topic-Based Search via Hierarchical NMF
%A Grotheer, Rachel
%A Huang, Longxiu
%A Huang, Yihuan
%A Kryshchenko, Alona
%A Kryshchenko, Oleksandr
%A Li, Pengyu
%A Li, Xia
%A Rebrova, Elizaveta
%A Ha, Kyung
%A Needell, Deanna
%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 grotheer-etal-2020-covid
%X A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.
%R 10.18653/v1/2020.nlpcovid19-2.4
%U https://aclanthology.org/2020.nlpcovid19-2.4
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.4
Markdown (Informal)
[COVID-19 Literature Topic-Based Search via Hierarchical NMF](https://aclanthology.org/2020.nlpcovid19-2.4) (Grotheer et al., NLP-COVID19 2020)
ACL
- Rachel Grotheer, Longxiu Huang, Yihuan Huang, Alona Kryshchenko, Oleksandr Kryshchenko, Pengyu Li, Xia Li, Elizaveta Rebrova, Kyung Ha, and Deanna Needell. 2020. COVID-19 Literature Topic-Based Search via Hierarchical NMF. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.