关于用于新冠病毒研究的自然语言处理模型的综述。
A review on Natural Language Processing Models for COVID-19 research.
作者信息
Hall Karl, Chang Victor, Jayne Chrisina
机构信息
SCEDT, Teesside University, UK.
Operations Information Management, ABS, Aston University, UK.
出版信息
Healthc Anal (N Y). 2022 Nov;2:100078. doi: 10.1016/j.health.2022.100078. Epub 2022 Jul 19.
This survey paper reviews Natural Language Processing Models and their use in COVID-19 research in two main areas. Firstly, a range of transformer-based biomedical pretrained language models are evaluated using the BLURB benchmark. Secondly, models used in sentiment analysis surrounding COVID-19 vaccination are evaluated. We filtered literature curated from various repositories such as PubMed and Scopus and reviewed 27 papers. When evaluated using the BLURB benchmark, the novel T-BPLM BioLinkBERT gives groundbreaking results by incorporating document link knowledge and hyperlinking into its pretraining. Sentiment analysis of COVID-19 vaccination through various Twitter API tools has shown the public's sentiment towards vaccination to be mostly positive. Finally, we outline some limitations and potential solutions to drive the research community to improve the models used for NLP tasks.
这篇综述论文回顾了自然语言处理模型及其在两个主要领域的新冠肺炎研究中的应用。首先，使用BLURB基准对一系列基于Transformer的生物医学预训练语言模型进行了评估。其次，对围绕新冠肺炎疫苗接种的情感分析中使用的模型进行了评估。我们筛选了从PubMed和Scopus等各种数据库中整理的文献，并审查了27篇论文。当使用BLURB基准进行评估时，新颖的T-BPLM BioLinkBERT通过将文档链接知识和超链接纳入其预训练中，给出了开创性的结果。通过各种Twitter API工具对新冠肺炎疫苗接种进行的情感分析表明，公众对疫苗接种的态度大多是积极的。最后，我们概述了一些局限性和潜在的解决方案，以推动研究界改进用于自然语言处理任务的模型。
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