Automatic Textual Evidence Mining in COVID-19 Literature

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

Research output: Working paper

Abstract

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.
Original languageEnglish (US)
StateIn preparation - Apr 27 2020

Publication series

NamearXiv:2004.12563

Keywords

  • cs.IR

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