# COVID-19 Multidimensional Kaggle Literature Organization

arXiv: https://arxiv.org/abs/2107.08190 · HTML: https://mlsanity.com/paper/2107.08190/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 17 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Distributed Non-Negative Tensor Train Decomposition](https://mlsanity.com/paper/2008.01340/) (2020)
- [COVID-19 Literature Topic-Based Search via Hierarchical NMF](https://mlsanity.com/paper/2009.09074/) (2020)
- [COVID-19 Kaggle Literature Organization](https://mlsanity.com/paper/2008.13542/) (2020)

## Abstract (arXiv)

The unprecedented outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, continues to be a significant worldwide problem. As a result, a surge of new COVID-19 related research has followed suit. The growing number of publications requires document organization methods to identify relevant information. In this paper, we expand upon our previous work with clustering the CORD-19 dataset by applying multi-dimensional analysis methods. Tensor factorization is a powerful unsupervised learning method capable of discovering hidden patterns in a document corpus. We show that a higher-order representation of the corpus allows for the simultaneous grouping of similar articles, relevant journals, authors with similar research interests, and topic keywords. These groupings are identified within and among the latent components extracted via tensor decomposition. We further demonstrate the application of this method with a publicly available interactive visualization of the dataset.