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Automatic Textual Evidence Mining in COVID-19 Literature

Published time: 27 April 2020

Authors: Xuan Wang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Jiawei Han

Keywords: mining, annotation, functionalities, automatically, textual evidence, EVIDENCEMINER, COVID-19


We created this EVIDENCEMINER system for automatic textual evidence mining in COVID19 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.

Automatic Textual Evidence Mining in COVID-19 Literature



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