# Automatic Textual Evidence Mining in COVID-19 Literature

arXiv: https://arxiv.org/abs/2004.12563 · HTML: https://mlsanity.com/paper/2004.12563/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Diachronic Text Mining Investigation of Therapeutic Candidates for COVID-19](https://mlsanity.com/paper/2110.13971/) (2021)
- [Fine-Grained Named Entities for Corona News](https://mlsanity.com/paper/2404.13439/) (2024)

## Abstract (arXiv)

We created this EVIDENCEMINER system for automatic textual evidence mining in COVID-19 literature. EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. It is constructed in a completely automated way without any human effort for training data annotation. EVIDENCEMINER is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The named entities and meta-patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization.