A Text Mining Approach to Discovering COVID-19 Relevant Factors

被引:0
|
作者
Sastre, Javier [1 ]
Vahid, Ali Hosseinzadeh [1 ]
McDonagh, Caitlin [1 ]
Walsh, Paul [1 ]
机构
[1] Accenture, Analyt & AI, Dublin, Ireland
关键词
Lucene; GrapeNLP; CORD-19; COVID-19;
D O I
10.1109/BIBM49941.2020.9313149
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
This paper describes a text mining approach that utilises the PyLucene search engine and the GrapeNLP grammar engine for extracting links between temperature, humidity and the spread of COVID-19, from a vast collection of scientific publications. The approach was developed in response to a Kaggle challenge from a consortium of research groups to develop text and data mining techniques that can assist the medical community in finding answers to a series of important questions on COVID-19. For this challenge, a large corpus of scientific publications known as the COVID-19 Open Research Dataset (CORD-19) was provided by the consortium. The approach presented in this paper was winner of the competition task of extracting key insights and building summary tables of COVID-19 relevant factors such as temperature and humidity.
引用
收藏
页码:486 / 490
页数:5
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