arXiv Open Access 2020

COVID-19 Literature Topic-Based Search via Hierarchical NMF

Rachel Grotheer Yihuan Huang Pengyu Li Elizaveta Rebrova Deanna Needell +5 lainnya

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Abstrak

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.

Topik & Kata Kunci

cs.DL cs.IR cs.LG stat.ML

Penulis (10)

Rachel Grotheer

Yihuan Huang

Pengyu Li

Elizaveta Rebrova

Deanna Needell

Longxiu Huang

Alona Kryshchenko

Xia Li

Kyung Ha

Oleksandr Kryshchenko

Format Sitasi

APA MLA BibTeX

Grotheer, R., Huang, Y., Li, P., Rebrova, E., Needell, D., Huang, L. et al. (2020). COVID-19 Literature Topic-Based Search via Hierarchical NMF. https://arxiv.org/abs/2009.09074

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓