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2020 EMNLP EMNLP 2020

Developing a Curated Topic Model for COVID-19 Medical Research Literature

Abstract

AbstractTopic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.

🌉 Interdisciplinary Bridge — Healthcare & Medicine and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — curated topic model
🐝 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

Philip Resnik , Katherine E. Goodman , Mike Moran

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Unsupervised Learning Healthcare & Medicine > Research > Bioinformatics Machine Learning > Core Methods > Topic Modeling Natural Language Processing > Applications > Topic Modeling

Keywords

latent dirichlet allocation text mining topic modeling information retrieval document clustering knowledge discovery medical literature document collection curated topic model
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