对用于COVID-19研究的自然语言处理模型的审查.
Karl Hall1, Victor Chang2, Chrisina Jayne1
1SCEDT, Teesside University, UK.
Healthcare analytics (New York, N.Y.)
|July 31, 2023
概括
本综述探讨了用于COVID-19研究的自然语言处理 (NLP) 模型,发现BioLinkBERT在生物医学任务中表现出色,并且对COVID-19疫苗接种的公众情绪在很大程度上是积极的.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算语言学 计算语言学
背景情况:
- COVID-19研究越来越多地利用自然语言处理 (NLP) 模型.
- 评估NLP模型对于推动生物医学研究和公共卫生见解至关重要.
研究的目的:
- 调查和评估用于COVID-19研究的NLP模型.
- 用BLURB基准来评估基于变压器的生物医学语言模型.
- 分析有关COVID-19疫苗接种的情绪分析模型.
主要方法:
- 在PubMed和Scopus发表的27篇论文的文献综述.
- 在BLURB基准上对生物医学语言模型的评估.
- 使用Twitter API数据分析情绪分析模型的分析.
主要成果:
- 通过整合文档链接知识,T-BPLM BioLinkBERT模型在BLURB基准上表现出卓越的表现.
- 在Twitter上对COVID-19疫苗接种讨论的情绪分析表明,公众情绪主要是积极的.
- 确定了COVID-19研究当前NLP模型的局限性和建议解决方案.
结论:
- NLP模型,特别是BioLinkBERT,在COVID-19生物医学研究中显示出重大前景.
- 情绪分析揭示了公众对COVID-19疫苗接种的普遍积极看法.
- 需要进一步开发以加强NLP模型的能力,以应对未来的公共卫生挑战.
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