2020
EMNLP
EMNLP 2020
Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration
Abstract
AbstractEfficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and propose several ways to improve topic coherence and specificity.
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Interdisciplinary Bridge
— Healthcare & Medicine and Machine Learning and Natural Language Processing
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Keyword Pioneer
— medical document
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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