论文标题
COVID-19通过层次NMF基于主题的文献搜索
COVID-19 Literature Topic-Based Search via Hierarchical NMF
论文作者
论文摘要
编译了与COVID-19相关的科学文献的数据集，结合了来自几个在线图书馆的文章，并选择具有开放访问和全文的人。然后，层次的非负矩阵分解用于将与新型冠状病毒相关的文献组织为树结构，使研究人员可以基于检测到的主题搜索相关文献。我们在文献体系中发现了八个主要的潜在主题和52个粒状子主题，与疫苗，遗传结构和疾病和患者研究的建模有关，以及相关疾病和病毒学。为了使我们的工具可以帮助当前的研究人员，创建了一个交互式网站，该网站使用这种层次结构来组织可用的文献。
A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.
