# COVID-19 Literature Topic-Based Search via Hierarchical NMF

arXiv: https://arxiv.org/abs/2009.09074 · HTML: https://mlsanity.com/paper/2009.09074/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents](https://mlsanity.com/paper/2201.13324/) (2022)
- [COVID-19 Multidimensional Kaggle Literature Organization](https://mlsanity.com/paper/2107.08190/) (2021)
- [Learning nonnegative matrix factorizations from compressed data](https://mlsanity.com/paper/2409.04994/) (2024)

## Abstract (arXiv)

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.