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COVID-19 Literature Topic-Based Search via Hierarchical NMF

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arxiv 2009.09074 v1 pith:AFNFKNRM submitted 2020-09-07 cs.DL cs.IRcs.LGstat.ML

COVID-19 Literature Topic-Based Search via Hierarchical NMF

Rachel Grotheer , Yihuan Huang , Pengyu Li , Elizaveta Rebrova , Deanna Needell , Longxiu Huang , Alona Kryshchenko , Xia Li
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Kyung Ha Oleksandr Kryshchenko
This is my paper
classification cs.DL cs.IRcs.LGstat.ML
keywords literaturehierarchicalrelatedstructureavailableresearcherssearchtopics
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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