关于COVID-19的文章的元数据足以完成多标签主题分类任务？
Is metadata of articles about COVID-19 enough for multilabel topic classification task?
影响因子:3.60000
分区:生物学4区 / 数学与计算生物学4区
发表日期:2024 Oct 21
作者:
Shuo Xu, Yuefu Zhang, Liang Chen, Xin An
摘要
COVID-19与19的文章的不断增加有关，这对Litcovid的手动策展和多标签主题分类提出了重大挑战。为此，本研究中开发了一种新型的多标签主题分类框架，该框架考虑了主题标签的相关性和不平衡，同时赋予了预验证的模型。在此框架的帮助下，这项研究致力于回答以下问题：与元数据（标题，摘要，关键字和期刊名称）相比，有关COVID-19的文章的文章是否编码更多的歧视信息？从我们丰富版本的BC7-Litcovid语料库和癌症语料库的标志的广泛实验中，可以得出以下结论。我们的框架表现出卓越的性能和鲁棒性。有关Covid-19的科学出版物的元数据提供了多标签主题分类的有价值的信息。与生物学实体相比，全文和网格可以进一步提高我们进行多标签主题分类框架的性能，但是改进的性能非常有限。数据库URL：https：//github.com/pzczxs/enriched-bc7-litcovid。
Abstract
The ever-increasing volume of COVID-19-related articles presents a significant challenge for the manual curation and multilabel topic classification of LitCovid. For this purpose, a novel multilabel topic classification framework is developed in this study, which considers both the correlation and imbalance of topic labels, while empowering the pretrained model. With the help of this framework, this study devotes to answering the following question: Do full texts, MeSH (Medical Subject Heading), and biological entities of articles about COVID-19 encode more discriminative information than metadata (title, abstract, keyword, and journal name)? From extensive experiments on our enriched version of the BC7-LitCovid corpus and Hallmarks of Cancer corpus, the following conclusions can be drawn. Our framework demonstrates superior performance and robustness. The metadata of scientific publications about COVID-19 carries valuable information for multilabel topic classification. Compared to biological entities, full texts and MeSH can further enhance the performance of our framework for multilabel topic classification, but the improved performance is very limited. Database URL: https://github.com/pzczxs/Enriched-BC7-LitCovid.