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2020 EMNLP EMNLP 2020

COVID-19 Literature Topic-Based Search via Hierarchical NMF

Abstract

AbstractA 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.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Healthcare & Medicine and Machine Learning
🐣 Hot Topic Early Bird — hierarchical clustering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio

Authors

Rachel Grotheer , Longxiu Huang , Yihuan Huang , Alona Kryshchenko , Oleksandr Kryshchenko , Pengyu Li , Xia Li , Elizaveta Rebrova , Kyung Ha , Deanna Needell

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Core Methods > Representation Learning Machine Learning > Learning Types > Unsupervised Learning Healthcare & Medicine > Research > Bioinformatics Data Science & Analytics > Methods > Data Mining Data Science & Analytics > Applications > Information Retrieval Machine Learning > Core Methods > Topic Modeling

Keywords

topic modeling hierarchical clustering nonnegative matrix factorization non-negative matrix factorization latent topic discovery document organization covid-19 literature
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