Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration
Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration
复制标题
改进医疗文档的主题表示以协助 COVID-19 文献探索
DOI：
10.18653/v1/2020.nlpcovid19-2.12
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Jey Han Lau
中科院分区：
文献类型：
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作者：
Yulia Otmakhova;K. Verspoor;Timothy Baldwin;Simon Suster;Jey Han Lau
在COVID-19大流行的背景下，有效发现和探索生物医学文献变得越来越重要，而基于主题的方法（如潜在狄利克雷分配（LDA））是实现这一目的的有用工具。在这项研究中，我们比较了传统的主题模型的基础上的单词令牌和主题模型的基础上的医学概念，并提出了几种方法来提高主题的连贯性和特异性。
Efficient 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 pro-pose several ways to improve topic coherence and specificity.