A Text Mining Approach to Discovering COVID-19 Relevant Factors
作者： Javier Sastre , Ali Hosseinzadeh Vahid , McDonagh , Caitlin , Paul Walsh
DOI: 10.1109/BIBM49941.2020.9313149
关键词:
摘要: This paper describes a text mining approach that utilises the PyLucene search engine and GrapeNLP grammar for extracting links between temperature, humidity spread of COVID-19, from vast collection scientific publications. The was developed in response to Kaggle challenge consortium research groups develop data techniques can assist medical community finding answers series important questions on COVID-19. For this challenge, large corpus publications known as COVID-19 Open Research Dataset (CORD-19) provided by consortium. presented winner competition task key insights building summary tables relevant factors such temperature humidity.
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