# COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research

arXiv: https://arxiv.org/abs/2008.07880 · HTML: https://mlsanity.com/paper/2008.07880/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [COVID-19 Named Entity Recognition for Vietnamese](https://mlsanity.com/paper/2104.03879/) (2021)
- [Linguistic Inspired Graph Analysis](https://mlsanity.com/paper/2105.06216/) (2021)
- [Impact of detecting clinical trial elements in exploration of COVID-19 literature](https://mlsanity.com/paper/2105.12261/) (2021)
- [An Information Retrieval and Extraction Tool for Covid-19 Related Papers](https://mlsanity.com/paper/2401.16430/) (2024)

## Abstract (arXiv)

We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest. We developed this system over COVID-19 literature to help medical professionals and researchers explore the literature evidence, and improve findability of relevant information. COVID-SEE is available at http://covid-see.com.