# COVID-19 Multidimensional Kaggle Literature Organization

### Abstract

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.

Publication
In ACM Symposium on Document Engineering 2021 (DocEng ’21), 2021

### Keywords:

COVID-19, tensor factorization, CP decomposition, document organization

### Citation:

Maksim E. Eren, Nick Solovyev, Chris Hamer, Renee McDonald, Boian S. Alexandrov, and Charles Nicholas. 2021. COVID-19 Multidimensional Kaggle Literature Organization. In ACM Symposium on Document Engineering2021 (DocEng ’21), August 24–27, 2021, Limerick, Ireland. ACM, New York, NY, USA, 4 pages. DOI:https://doi.org/10.1145/3469096.3474927

### BibTeX:

@inproceedings{10.1145/3469096.3474927,
author = {Eren, Maksim Ekin and Solovyev, Nick and Hamer, Chris and McDonald, Renee and Alexandrov, Boian and Nicholas, Charles},
title = {COVID-19 Multidimensional Kaggle Literature Organization},
year = {2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3469096.3474927},
doi = {10.1145/3469096.3474927},
abstract = {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.},
booktitle = {Proceedings of the ACM Symposium on Document Engineering 2021},
numpages = {4},
location = {Virtual Event, Limerick, Ireland},
series = {DocEng '21}
}